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Assessing the impact of model formulation and resolution on Arctic sea ice variability and regional predictability

Principal Investigator(s): Rym Msadek & Gabriel Vecchi & Michael Winton, NOAA/GFDL

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: | View Publications on Google Scholar


Rapid and dramatic changes that can have profound impacts on human activities and ecosystems have been observed in the Arctic over the past decades. As a result, there has been increased attention into understanding the trend towards decline of Pan-Arctic sea ice. However, there has been very less research into the systematic predictability of the coupled climate system in the Arctic, in particular at regional scales. The goal of this project is to improve our understanding of the dynamical processes controlling seasonal Arctic sea ice variations to identify the processes that are source of predictability on regional scales. More specifically, we seek to identify the dominant sources of Arctic sea ice variability and predictability during both summer and winter and characterize regional differences in the dominant processes. In addition to the year-to-year variability we will investigate regional differences in the character and mechanisms of the multi-year to multidecadal sea-ice changes. We plan to use the latest generation of state-of-the-art climate models developed at GFDL. We will compare models that have overly thin ice in the Arctic (CM2.1, 1o ocean, 2o atmosphere) to more realistic models that simulate thicker sea ice because of an improved atmosphere (FLOR, 1o ocean, 50km atmosphere; CM3, 1o ocean, 2o atmosphere with improved chemistry) or an improved ocean (CM2.5, 0.25o ocean, 50km atmosphere, CM2.6, 0.1o ocean, 50 km atmosphere) or because it assimilates data (ECDA). We will use control simulations of some of these climate models as well as perfect model predictability runs and quasi-operational initialized forecasts based on selected models. These models have significant differences in their mean state and variability, which will allow us to explore the impact of mean state and model configuration on Arctic sea ice variability and predictability, and to better understand the mechanisms of regional Arctic variability.

The proposed work is highly relevant to the CVP competition of the FY 2015 call on Understanding Arctic sea ice mechanisms and predictability. This proposal addresses one of NOAA strategic goals for the Arctic region, to provide forecasts of sea ice and understand and detect climate and ecosystem changes. Because of our close working relationships with the in-house developers of the model and forecast systems we plan to use, support for this proposal would likely have a direct impact on climate simulations and predictions at NOAA.

Extreme moisture transport (atmospheric rivers) into the Arctic and its effect on sea-ice concentration

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

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310164 | View Publications on Google Scholar


Over recent decades the Arctic has warmed approximately twice as fast as the rest of the Northern Hemisphere. At the same time, Arctic sea-ice concentration has decreased rapidly, especially in September when sea ice in the Arctic reaches its lowest extent of the year. Interannual variability in the minimum sea ice extent is enormous, especially over the past decade that includes several years of record minimum coverage interspersed with other less extreme years. Satellite observations of sea ice concentrations go back to 1979.

This vast interannual variability is mostly driven by extratropical atmospheric dynamical processes both directly and indirectly, and modulated by slower ocean processes. Wind represents an important forcing of sea ice distribution that qualifies as direct forcing. Thermodynamical consequences of extratropical dynamical variability such as changes to the radiative surface fluxes due to increased moisture in the Arctic can in turn lead to important feedback processes that can quickly amplify the change. A recent study indicates that in years when there is a low Arctic sea-ice minimum in September there is an increase in moisture transport into the Arctic in the preceding spring. The increase in moisture leads to increased greenhouse effect that is thought to play an important role in initiating the melt in spring that will become an extensive area of melt in September. We hypothesize that the extreme moisture transport into the Arctic in the form of atmospheric rivers (ARs) during certain key parts of the year plays an important role in the extent of the sea-ice minimum that is reached each year, and overall in interannual variability of sea ice concentrations.

We propose to analyze the frequency and moisture flux of ARs in certain key areas of the Arctic in 35 years of reanalysis data, the time period of which overlaps with observations of sea-ice concentration. We will examine sea-ice concentration and surface fluxes following episodes of extreme moisture flux, as well as the large-scale flow because of the close association of ARs to Rossby wave breaking, a process that drives major climate patterns. We will carry out similar analysis for the archive of CMIP5 climate simulations, both historical runs and projections for the coming century under projected increases in greenhouse gases. We will test hypothesis regarding the role of ARs for Arctic sea ice concentration by running idealized Global Climate Model simulations.

The work is directly relevant to the opportunity in that it examines climate mechanisms that affect Arctic temperatures and variability in sea-ice concentration in observations and model simulations. This will lead to an improved scientific understanding of the changing climate system, which is a stated goal of NOAA’s Next Generation Strategic Plan.

Using Snow Cover to Advance Sea Ice Forecast Models

Principal Investigator(s): Julienne Stroeve & Mark Serreze & Andrew Slater, University of Colorado, Boulder

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310171 | View Publications on Google Scholar


Due to the rapid decline in Arctic sea ice extent and volume over the past decade, there has been a growing focus on developing capabilities for prediction. NOAAΓÇÖs Arctic Action Plan calls for an improvement in sea ice predictability ranging from the short term (e.g. daily and weekly) to seasonal to decadal time-scales. Such an effort is particularly important in light of the large variability seen in annual sea ice minima. To gain predictive skill, one must gain an understanding of sea ice variability and the coupled terrestrial, ocean and atmospheric systems that influence this variability.

We propose to advance the understanding of Arctic sea ice variability and predictability by investigating several interrelated items that have been largely overlooked, but that we hypothesize will give further insight to the seasonal fate of sea ice. These items include (a) the influence of spring and early summer snow cover over Northern Hemisphere lands, (b) the atmospheric circulation patterns that favor ice melt, their precursors and mechanisms by which the atmosphere interacts with snow to impact the sea ice and (c) the ability of models to capture observed relationships. We will also explore additional relationships between the sea ice melt season and quantities such as melt onset date, atmospheric moisture content, and winter ice dynamics

.Speculation regarding relationships between terrestrial snow and sea ice dates back at least 20 years, but there has been no systematic investigation of mechanisms relating the two quantities at regional scales. To fill this gap, we will apply innovative tools such as complex network analysis, which can provide insight into the spatial relationships between various nodes (in this case, gridboxes of snow and ice cover) of a network (the snow and sea ice system). Using such techniques we will search for predictive power amongst snow variables, as well as a multitude of information sources such as atmospheric circulation patterns or ice melt onset date. NOAA climate data records and reanalysis products (e.g. NOAAΓÇÖs CFSR) will be used in our analysis. We also propose to gain an understanding of model abilities, particularly the CMIP5 models and the NOAA Climate Forecast System (CFS), by determining whether they can reproduce observed linkages. Model deficiencies can point to structural issues, which in turn can lead to improvements. Results from the SEARCH Sea Ice Outlook (SIO; now part of the new Sea Ice Prediction Network (SIPN) led by PI J. Stroeve) indicate that there is much room for improvement within current modeling and prediction systems.

This work is directly responsive to this NOAA proposal call in that it seeks to understand where predictability can arise from and how that understanding may be applied in a forecasting context. Greater understanding of sea ice within the Arctic system is one of the goals of NOAAΓÇÖs Next-Generation Strategic Plan and its central mission to understand and predict changes in climate, weather, oceans and coasts.

Sea Ice Mechanics and Ice Thickness Distribution: Development, Evaluation & Application of an Elastic Decohesive Sea Ice Model

Principal Investigator(s): Deborah Sulsky, University of New Mexico

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310165 | View Publications on Google Scholar


The shrinking extent and thickness of the Arctic sea ice cover, as well as the major loss of multi-year pack ice is allowing greater access to the Arctic. In order to make use of this new accessibility efficiently and to guarantee safe operations, high-resolution sea ice forecasts are required on a variety of time scales, from hours to days, months, and seasons. Currently, shortcomings in our modeling capability preclude accurate prediction of ice characteristics on the necessary variety of temporal and spatial scales. The ability to simulate the ice edge, the space and time evolution of the pack ice, as well as ice types, thickness distribution, strength and state of deformation is crucial to accurate sea ice forecasting.

We propose to improve the representation of sea ice mechanics in general circulation models and Earth system models in order to advance the short-term (days to months), high- resolution (tens of meters to kilometers) predictive capabilities of these models. We will pursue this objective by expanding, evaluating, and applying a new sea ice model, which describes sea ice mechanics based on the elastic-decohesive rheology. In contrast to the isotropic, viscous-plastic rheology of most current sea ice models, the elastic-decohesive rheology explicitly represents the presence and direction of sea ice deformation features. The specific focus of the present proposal is on ‘understanding’ rather than ‘prediction’; that is, we aim to demonstrate that the elastic-decohesive model can better describe the underlying mechanisms responsible for regional sea ice variation and change through comparison with satellite and in-situ observations of sea ice deformation. If successful, however, this project has the potential to deliver models with much improved predictive capability of high-resolution, coupled ocean, wave, atmosphere, and sea ice processes.

The proposed work falls into three categories: model development, model evaluation, and the application of the new model. Model development focuses on the connection of an ice thickness distribution to the decohesive rheology and implementation of the rotation of the lead direction as leads are advected with the flow of the pack ice. Further, we propose a mechanism to account for the increasing strength of ice growing in leads. These developments are crucial to correctly represent the anisotropy of the ice strength, which impacts the large-scale flow of the ice cover and its local deformation. Model evaluation begins with a comparison to observations of ice concentration, thickness, and drift but focuses on the deformation rate and lead statistics. A variety of airborne and satellite derived data sets will be used. Finally, we will apply the new model in a suite of sensitivity simulations to test its dependency on spatial resolution as well as its ability to simulate break-up events of the sea ice cover in late winter and spring. Timing and spatial extent of ice beak-up are relevant to both climate processes and operational decision making.

This proposal is being submitted in response to the NOAA call, Climate Variability and Predictability (CVP) FY 2015: Understanding Arctic Sea Ice Mechanisms and Predictability. The proposed model supports NOAA’s mission to assess current and future states of the climate system in order to identify potential impacts and inform science, service, and stewardship decisions. The model specifically aims to advance the CVP program goal of understanding Pan-Arctic sea ice interactions by better describing mechanisms involved in prediction of regional sea ice variation and change.

The predictability of extreme Arctic sea ice variations in a rapidly changing climate

Principal Investigator(s): Stephen Vavrus, University of Wisconsin, Madison; Marika Holland, NCAR; Muyin Wang, JISAO, University of Washington

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310166 OR NA15OAR4310167 OR NA15OAR4310168 | View Publications on Google Scholar


Arctic sea ice has been diminishing dramatically in recent years, reaching a record low in 2012 after a previous sudden drop in 2007. Climate model simulations also generate such large and rapid summertime ice loss events in the near future. However, the natural variability of the Arctic system is very high, and models exhibit instances of increasing sea ice cover even well into the 21st century. Improved understanding of the character, impacts, and potential forecasting of these types of extreme sea ice events has great societal relevance for Arctic marine access, seasonal forecasting, and climate variability.

In this project we will assess the processes responsible for, and the predictability of, rapid Arctic sea ice variations, with an emphasis on the implications for marine navigation and extreme weather. We will utilize the Community Earth System Model 1 (CESM1), one of the better models from the CMIP3/CMIP5 archives. CESM1 simulates 20th-century Arctic sea ice very realistically and will be used to examine the predictability of Arctic sea ice cover, especially rapid and extreme sea ice variations. We will assess the regional nature of this predictability and the impacts of expanding open water coverage on extreme weather. Our proposed research will contribute to the recently funded Sea Ice Prediction Network (SIPN), which is partially supported by NOAA.

This project will center around five hypotheses and questions regarding Arctic sea ice and its changing variability. We expect that in the future: (1) The variability of ice area will increase and its predictability will decrease, (2) Expanding open water area will promote more extreme cyclones, and (3-4) Regional sensitivity to extreme sea ice variations will change, as will the predictability of ice conditions relevant for marine access. We will also address (5) What are the observational network requirements to realize the predictive capability of sea ice conditions?

Through testing these hypotheses this project will advance our knowledge in all three priorities of the CVP competition: identifying the mechanisms responsible for (extreme) sea ice variations (hypothesis 1 and 4); focusing on regional mechanisms and predictability of sea ice variability, which are highly relevant for marine access in the Arctic (hypotheses 3 and 5); and identifying the key driving factors under changing climatic conditions within a fully coupled modeling framework (hypotheses 1, 4, and 5). By improving predictive understanding of Arctic variability and extreme events, our research aligns with the objectives in NOAA's Next Generation Strategic Plan (NGSP).

We will test these hypotheses with the CESM-CAM5 model and its large ensemble simulations(30+) spanning nearly two centuries (1920-2100), in addition to a comparison with other CMIP5 models. Our research complements and enhances SIPN through the predictability of extreme sea ice variations and their associated drivers/impacts. We will use a variety of metrics to quantify predictability and analyze the associated atmospheric and oceanic conditions Some outcomes of this project will be: (1) Identification of Arctic regions most susceptible to extreme sea ice variations, (2) Improved understanding of the predictability of Arctic marine access, and (3) Assessment of observational network requirements and extreme weather impacts.

An analog system to enhance seasonal predictions of sea ice

Principal Investigator(s): John Walsh, University of Alaska, Fairbanks

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310169 | View Publications on Google Scholar


Given the importance of wind and temperatures for the evolution of sea ice anomalies, we propose to develop a monthly-to-seasonal analog forecasting system for sea level pressures and temperatures over the Arctic. This approach departs from the conventional statistical methodologies (e.g., screening regression) and dynamical model forecasts. Our rationale is that neither of the latter two approaches, widely used in seasonal sea ice outlooks, has shown the ability to capture large year-to-year changes of summer ice extent in recent years. The use of an analog approach to forecasts of atmospheric forcing therefore offers a novel and potentially useful approach to improved seasonal sea ice forecasts of pan-Arctic and regional sea ice extent.

The proposal is submitted to the Climate Variability Program (CVP) competition "CVP - Understanding Arctic Sea Ice Mechanisms and Predictability". It directly addresses the program element "Mechanisms, predictability and prediction of regional sea ice variation and change". By targeting improvements in seasonal sea ice prediction for pan-Arctic and regional ice extent, the project responds to the NOAA Next Generation Strategic Plan’s objective #2: "Assessments of current and future states of the climate system that identify potential impacts and inform science, service and stewardship decisions".

Our analog selection will be based on the current fields of the atmospheric circulation (sea level pressure, 500 hPa geopotential), indices of major atmospheric teleconnections (Arctic Oscillation, El Nino/Southern Oscillation, Pacific North American pattern, Pacific Decadal Oscillation) and the evolution of these fields and indices over the immediate preceding period of ~1 week. The atmospheric fields will be from the reanalysis products of the National Centers for Environmental Prediction (NCEP). The weights used for the different variables in the analog selection process will be based on the errors, spatially aggregated over the Arctic, of seasonal hindcasts for the 1948-2014 period of the NCEP reanalysis. We will experiment with different numbers of analogs to be composited into the seasonal forecast fields; the various analogs will be weighted to produce constructed analogs. Analog forecast systems will be developed for pan-Arctic ice extent and ice extent in 14 Arctic subregions.

The skill of the analog forecasts of the seasonal pressure and temperature fields will be evaluated over the Arctic Ocean and subarctic seas, and will be compared with the corresponding metrics from the Coupled Forecast System (CFS) that is run routinely at NCEP, as well as the North American Multi-Model Ensemble (NMME) that is distributed by the Climate Prediction Center. We will evaluate the skill as a function of season and sector of the Arctic/subarctic marine domain where at least seasonal sea ice occurs. We will document the impact of the analog forecast system on seasonal sea ice forecasts derived from a dynamical ocean-ice model.

We will produce an experimental forecast website displaying the forecasts, updated weekly, during the final year of the project after the system is optimized. The analog visualization site will leverage the user interface and display functions of the Arctic Collaboration Environment (ACE), which recently transitioned to a new home at the University of Alaska, Fairbanks. The display will include both the atmospheric forecasts (presented as actual fields and as departures from normal) superimposed on the current sea ice field and its departures from normal. The ACE system provides the capability for overlaying layers of other variables.

Collaborators will include the National Weather Service’s Alaska Region (Richard Thoman), with whom we will explore additional uses of the analog forecast system. At least one journal publication, in addition to the project's deliverables.

Improving Initialization of Arctic Sea Ice in NCEPΓÇÖs Climate Forecast System for Advancing Long-Range Predictions

Principal Investigator(s): Wanqiu Wang, NOAA/NCEP; Jinlun Zhang, University of Washington

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310170 | View Publications on Google Scholar


Prediction and predictability of Arctic sea ice on different time scales has received increasing attention recently. While “perfect-model” studies have shown that Arctic sea ice extent is predictable out to eight months or longer, diagnoses of the forecasts from the current dynamical operational climate models show that the useful prediction skill for interannual sea ice anomalies is lost beyond the first 2-3 months. Similarly, the analysis of the outlook collected by the NOAA SEARCH (Study of Environmental Arctic Change) indicates that predicting the variability of the September Arctic sea ice is difficult even from July initial conditions, and further, there exists a substantial spread among the forecasts from different prediction systems. Understanding the causes of the forecast errors and the discrepancy between the potential predictability and actual skill, and enhancing the skill of prediction of Arctic sea ice to at par with predictability estimates is a highly desirable goal to improve the scope and reliability of NOAA’s sea ice operational prediction capabilities.

Improvements in the skill of long-range forecasts for Arctic sea ice can stem from many sources. One such source is the correct initialization of sea ice thickness (SIT). Despite its perceived importance, however, the influence of the observed information in initial SIT on the prediction is not well incorporated in the current generation of operational forecast systems. As an example, the initial SIT in the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) is from the Climate Forecast System Reanalysis (CFSR) which contains substantial SIT errors. The erroneous initial SIT used in CFSv2 is an important factor limiting its sea ice prediction skill.

The main objectives of this proposal are to i) investigate the contribution of initial SIT to the prediction of Arctic sea ice in the NOAA's NCEP seasonal climate prediction system, and ii) improve its prediction skill by improving the SIT initialization. In addition, we will also analyze how the influence of initial SIT on prediction skill relates to the reduction of forecast model's systematic bias caused by uncertainties in the atmosphere-ice-ocean interactions. We will accomplish these objectives with the following activities:
1) Historical forecast experiments with an alternative initial SIT from the well calibrated Pan-arctic Ice/Ocean Modeling and Assimilation System (PIOMAS) to analyze improvements in sea ice prediction skill, and how the skill can be affected by forecast model's systematic bias;
2) Development of an improved initialization by adopting the approach of the PIOMAS in the CFSv2 ice/ocean component model to explore optimal sea ice parameterizations, and implement an approach similar to PIOMAS for improving SIT initialization in the fully coupled CFSv2; and
3) Forecast experiments with the new initial conditions from 2) to demonstrate the advantage of using an improved SIT initialization that is consistent with the forecast model for an improved sea ice prediction.

The proposed research will lead to a better sea ice forecasts to meet the requirement for the prediction of seasonal sea ice melting and freezing by operational institutions such as the NWS field offices in Alaska. The initialization procedure developed herein will also contribute to an improved sea ice prediction for NOAA's participation in the national SEARCH sea ice outlook and in the international collaboration for the Polar Prediction Project (PPP).

This project is based on the framework of the current operational CFSv2. The sea ice component in CFSv2 will continue to be used in the updated Modular Ocean Model version 5 (MOM5) which is expected to be the oceanic component for the next generation of the Climate Forecast System (CFSv3). Accordingly, the proposed research will not only result in an improved prediction using the current CFS framework but will also contribute to the continued improvement of CFS. The proposed research will also improve our understanding of sea ice prediction skill and predictability associated with initial SIT, which has been an active area of scientific research among the climate community.

The impact of meridional variations in cloud albedo on tropical climatology, and biases, in Earth system models

Principal Investigator(s): Alexey Fedorov, Yale University

Year Initially Funded: 2014

Program (s): Climate Variability and Predictability

Competition: Improved Understanding of Tropical Pacific Processes, Biases, and Climatology

Award Number: NA14OAR4310277 | View Publications on Google Scholar


A salient feature of the tropical Pacific is the pronounced east-west gradient in sea surface temperature along the equator. This temperature gradient is coupled to the atmospheric zonal circulation and oceanic thermocline tilt. Together they define the Warm pool, Cold tongue, Walker circulation Complex (WCWC). Asymmetry in the SST and precipitation fields about the equator sets the position of the Intertropical Convergence Zone (ITCZ). This is a proposal to study the effect of meridional variation in cloud albedo across the entire Pacific basin on this tropical climatological state in Earth system models.

Coupled model simulations of the tropical Pacific, while improved over the past decades, still show significant biases. A cold bias within the cold tongue region, a cold tongue that extends far west into the warm pool, and the double-ITCZ problem remain persistent issues in climate models. The mean east-west temperature gradient along the equator in the CMIP5 models varies between 3 and 8 degrees C, compared to nearly 6 degrees C in the observations. Developing a comprehensive understanding of what determines this temperature gradient and related characteristics of the fully coupled ocean-atmosphere system is critical for understanding tropical climatology and potential biases in climate models. Often, this question is treated as a local problem in the equatorial band – improvements are sought by tuning local parameters affecting the properties of deep convection in the warm pool or the amount of shortwave radiation reaching the eastern Pacific. However, the strength of the cold tongue is ultimately controlled by the temperature of waters subducted in the extra-tropics and transported to the equator by the ocean subtropical cells (STC). Consequently to understand tropical biases one needs to treat this coupled problem in a broader geographical context and consider latitudinal variations in the main dynamical factors.

Our preliminary analysis indicates that the meridional gradient in cloud albedo is one of these key factors. A close relationship exists across the pre-industrial CMIP5 simulations and our preliminary numerical experiments that connects the mean east-west gradient in upper-ocean temperatures and the contrast in cloud albedo between the extra-tropical and tropical Pacific. For example, when extra-tropical cloud albedo is higher than observed, or tropical cloud albedo is too low, the east-west temperature gradient is stronger than observed. Thus, we propose that in coupled climate models the zonal SST gradient and the related characteristics of the tropical ocean-atmosphere system (e.g. zonal winds and the thermocline tilt) are largely controlled by the meridional gradient in cloud albedo between the equator and the extra-tropics. Further, we propose that it is the inter-hemispheric albedo contrast that controls the position of the ITCZ. To investigate these problems we will conduct (i) sensitivity experiments with CESM, in which we systematically modify cloud properties affecting cloud albedo, (ii) a theoretical analysis of the coupled system with cloud feedbacks included, and (iii) an analysis of CMIP5 models focused on the effects of latitudinal variations in cloud albedo on the tropical climate.

Relevance to long-term NOAA goals and current solicitation: The overarching goal of this study is to understand the fundamental physical mechanisms that control tropical climatology and model biases. This objective is directly relevant to the current solicitation and longer-term NOAA goals, since simulating tropical climate correctly is critical for climate prediction on a variety of timescales from seasonal to interannual, to decadal and longer. Funding from this grant will support cross-disciplinary training of a postdoctoral associate at Yale, Dr. Natalie Burls.

A Global View of Tropical Pacific Biases and Their Effect on Connections Between the Southern Hemisphere and the Equatorial Pacific Climate

Principal Investigator(s): Amy Clement, University of Miami, RSMAS

Year Initially Funded: 2014

Program (s): Climate Variability and Predictability

Competition: Improved Understanding of Tropical Pacific Processes, Biases, and Climatology

Award Number: NA14OAR4310275 | View Publications on Google Scholar


Previous studies have shown that extra-tropical influences from the North Pacific can impact the tropical Pacific climate, offering some additional degree of predictability of El Nino/Southern Oscillation (ENSO). More recent work has also shown that similar physical mechanisms operate in the Southern hemisphere, and further suggest that the connection between the southern extra-tropics and the equatorial climate is even more direct than the north. The reason is that southeasterly trade winds cross the equator and allow signals to propagate from the south deep into the tropics, while in the northern hemisphere signals are essentially ‘blocked’ by the convergence of winds in the northern ITCZ. Simulating this connection from the southern hemisphere is problematic in coupled GCMS in which an erroneous southern ITCZ can potentially block extra-tropical signals originating in the south. Thus, in this project we will test the hypothesis:

That Pacific ITCZ biases in climate models weaken the southern hemisphere influence on the equator and diminish a potential source of ENSO predictability.

We will test this hypothesis using a collection of climate model simulations that offer multiple realizations of the mean state biases in the Pacific ITCZ. We will perform diagnostic studies using CMIP5 model simulations to test the model-dependence of the mean state and its influence on variability propagating from the southern hemisphere to the equator. We will perform a large number of experiments with climate models in which the ITCZ position is altered in two ways: First by externally imposed perturbations to the energy budget of the model, and second by altering the strength of regional radiative feedbacks using a novel approach that we have developed in prior work (funded by NOAA). This dual method approach of altering ITCZ builds upon recent work that suggests that in addition to local processes in the Pacific that can lead to biases in the ITCZ, the mean ITCZ position is also influenced by processes outside the tropics that alter the radiative balance of the planet. This experimental approach has the advantage of being able to (1) test which regions of the globe are key for the simulation of the Pacific ITCZ, and (2) examine mean state interactions with variability in a consistent framework. Further we will use a hierarchy of models including aqua planet models, AGCM-slab ocean mixed layer models, and fully coupled models, which will allow us to identify the fundamental mechanisms which control the position of the ITCZ and impact Pacific climate variability. The ultimate goal of this work is to design an experimental framework in which we can test how potential sources of predictability, particularly from the southern hemisphere, are affected by Pacific ITCZ biases.

This work will contribute to the goals of the ‘ESS - Climate Variability and Predictability (CVP): Improving Understanding of Tropical Pacific’ by identifying processes and regions outside the tropical Pacific that exert a remote influence on the mean climate. This will help to provide a more complete and global context for understanding tropical Pacific biases, and suggest novel ways in which models can be developed to reducing this external influence on these biases. A unique aspect of this work is that we are focused on additional (an perhaps unexploited) sources of predictability in the Pacific climate system, namely signals or precursors from the southern hemisphere. By testing the central hypothesis or this project, our work will enhance NOAA’s core capabilities in both ‘Modeling and understanding’ and in ‘Predictions’ of the Pacific climate system.

Understanding Tropical Pacific Biases in Climate Simulations and Initialized Predictions

Principal Investigator(s): Andrew Wittenberg, NOAA/GFDL; Gabriel Vecchi, NOAA/GFDL; Tom Delworth, NOAA/GFDL; Yan Xue, NCEP/CPC; Arun Kumar, NCEP/CPC;

Year Initially Funded: 2014

Program (s): Climate Variability and Predictability

Competition: Improved Understanding of Tropical Pacific Processes, Biases, and Climatology

Award Number: GC14-250a | View Publications on Google Scholar


We propose a collaborative study between GFDL and NCEP, to advance understanding, simulation, and forecasting of tropical Pacific climate and its variability. Motivated by the central role that the tropical Pacific plays in climate variability worldwide -- in particular via the El Niño / Southern Oscillation (ENSO) -- there is an urgent need to advance mechanistic understanding of the tropical Pacific climatology and its impacts on climate variability, and to improve the coupled general circulation models (CGCMs) upon which society relies for seasonal-to-interannual (SI) forecasts and decadal-to-centennial predictions and projections. A unique strength of this proposal is close coordination between two of NOAA’s premier institutions for simulation, assimilation, and prediction of tropical Pacific climate and ENSO. In particular, a critical aspect of the proposed work is the development of common metrics, and a coordinated design and analysis of focused simulation and forecast experiments leveraging next generation models. This coordination will facilitate assessment of the robustness of the model results and underlying mechanisms, to accelerate improvements in NOAA’s SI simulation and prediction capabilities. Our goals are to (1) diagnose the spatiotemporal structure of tropical Pacific climatological biases in GFDL’s and NCEP’s coupled simulations, reanalysis systems, and forecasts; (2) identify similarities and differences among GFDL’s and NCEP’s model biases, and understand how differences can be linked to model parameterizations, assimilation methods, and observational inputs; (3) understand the processes which seed and amplify tropical Pacific biases; (4) assess how these biases affect the simulation and prediction of climate fluctuations; and (5) develop methods to mitigate these biases and their impacts on forecast skill. We will use our findings to evaluate existing hypotheses for the emergence of tropical Pacific biases, and to assess the applicability of our results to the broader set of community models and forecasts, including those available from the CMIP5 and NMME projects.

Relevance: The proposed work is highly relevant to the NOAA CPO. We seek to improve scientific understanding and prediction of the climate system, by evaluating and advancing methodologies used for simulations and forecasts. We directly address several objectives of NOAA’s Next Generation Strategic Plan (NGSP), including (1) Improved scientific understanding of the changing climate system and its impacts, by elucidating the causes and effects of simulation biases, and advancing climate modeling, predictions, and projections; (2) An integrated environmental modeling system, by advancing fundamental climate research and transitioning it toward NOAA’s production of seasonal forecasts, by coordinating within NOAA to enhance the accuracy of global models and predictions, and by evaluating and optimizing NOAA’s investments in observation and monitoring through the use of models; and (4) A climate-literate public that understands its vulnerabilities to climate, by elucidating the strengths and limitations of climate information affected by simulation biases. Relevance to the Competition: This proposal directly addresses the ESS CVP solicitation. The analysis and multimodel experimentation will focus on understanding tropical Pacific biases in two of NOAA’s leading coupled GCMs, which are widely used for climate simulation, reanalysis, and predictions. The work will leverage nearly all of the methods suggested in the proposal call, including advanced physical metrics, reduced-model experiments, and short-term forecasts, to diagnose the sources and amplifiers of model biases.



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