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An Investigation of Abyssal to Mid-depth Variations in AMOC Properties and Transports through Observations and Assimilating Models

Principal Investigator(s): Alison Macdonald (Woods Hole Oceanographic Institution), Xujing Davis (Woods Hole Oceanographic Institution), Molly Baringer (NOAA/AOML)

Year Initially Funded: 2016

Program (s): Climate Variability and Predictability

Competition: AMOC-Climate Linkages in NA/SA

Award Number: NA16OAR4310172 | View Publications on Google Scholar

Statement of the Problem: To understand the causes of decadal-scale variability in Atlantic overturning waters it is necessary to both recognize and connect the changes that are occurring in a moving ocean. That is, from a climate perspective, there is a need to use available information to better understand not only how ocean properties are changing, but also how dynamics may be affecting changes. While models and re-analyses look to provide a moving and even predictive three-dimensional picture, both correct and incorrect details are often lost in integration of  available outputand deep signals, in particular, may be missing from numerical integrations. On the other hand, observations capable of providing details on the characteristics of deep ocean properties and processes are, more often than not, disconnected from one another in time.

Speaking to the AMOC competition’s aim to refine present knowledge of the AMOC state,  variability and change as well as NOAA’s long term goal of an improved understanding of the  changing climate system, the goals of this project are twofold: 1) to improve understanding of  changes in the deep South Atlantic Meridional Overturning Circulation (SAMOC) properties and transports through a statistical analysis and comparison of observations and numerical model  output, and 2) to develop an understanding of where and how two particular models are  succeeding and/or failing to capture observed deep signals thought to be signatures of climate  change. Repeat hydrographic lines will be used in combination with float data and Lowered Acoustic Doppler Current Profiles together with output from two high-resolution assimilative models (HYCOM and ECCO) to develop an understanding of what changes are occurring in the observed fields, where they are occurring, how such changes are or are not reflected in the numerical fields, and whether this matters. That is, what are the overall consequences to numerical prediction of discrepancies between modeled and observed deep and bottom water changes and transport? The observation-model comparative analysis is relevant to the AMOC competition’s aim of combining existing observations with models to refine our understanding of present-day and past AMOC circulation and transport. It is also relevant to NOAA’s goal of  informing future climate-scale predictions as it lo oks to determine the importance of specific  deep/abyssal pathways and particular regions of mixing to decadal simulations.

The proposed work includes a formal collaboration with Elaine McDonagh and colleagues at the  National Oceanography Centre in Southampton and informal collaborations with Edmo Campos  and SAMOC group working towards a long-term South Atlantic observational network, as well as Tonia Capuano at the Université de Bretagne Occidentale to assist in the calculation of mixing  estimates.

Understanding drivers and impacts of CGCM biases in representing the decadal variability of Labrador Sea convection

Principal Investigator(s): Annalisa Bracco (Georgia Tech), Takamitsu Ito (Georgia Tech)

Year Initially Funded: 2016

Program (s): Climate Variability and Predictability

Competition: AMOC-Climate Linkages in NA/SA

Award Number: NA16OAR4310173 | View Publications on Google Scholar

The Labrador Sea (LS) is one of the few regions in the world ocean where deep convection occurs. The intense air-sea interaction drives the convective mixing and the site acts as a window through which anthropogenic carbon is sequestered into the interior ocean. Recent work highlights that buoyancy forcing over the Labrador Sea is key in controlling the Atlantic Meridional Overturning Circulation (AMOC) and that AMOC inter-annual signals are closely related to the variability of the Labrador Sea convection. Historic observations collected over the past 60 years show that the LS convective activity undergoes dramatic interannual-to-decadal variability and –within the limitation of the available measurements – no statistically significant trend. A model integration performed using a regional ocean model (ROMS, Regional Oceanic Modeling System) run at 5km horizontal resolution over the LS can reproduce the observed variability. However, coupled general circulation models (CGCMs) from the Coupled Model  Intercomparison Project Phase 5 (CMIP5) are not yet capable of representing the extent and  statistical properties of the LS convection, while often displaying a weakening trend for the past  50 years. Model biases hamper the representation of the AMOC and of the inventories of dissolved inorganic carbon, and limit our ability to project their future changes.  The overarching objectives of this project are to diagnose the sources of CGCMs biases in the LS focusing on a subset of CMIP5 runs and to quantify the impacts of those biases on the representation of carbon uptake and inventories in the basin. They will be achieved through a sensitivity study to be performed using regional ocean-only ROMS simulations covering most of the North Atlantic forced by momentum, heat and freshwater fluxes, and/or boundary conditions from the CMIP5 runs.
This project will establish cause-effect linkages between the representation of mesoscale processes, of the atmospheric forcing fields, and of the gyre circulation, and the (modeled) Labrador Sea circulation, its variability and carbon uptake characteristics.  The regional simulations will include an ocean biogeochemical and carbon cycling module. The interpretation of all model analysis will be aided by careful comparisons with shipboard and Argo measurements in the Labrador Sea, and along 53N. In this regard we will build upon our ongoing collaboration with Dr I. Yashayeav at the Bedford Institute of Oceanography. This project will contribute a better understanding of the potential predictability of Labrador Sea convection and of the natural and anthropogenically forced variability of the AMOC.
This proposal addresses the objective of the NOAA funding opportunity, CVP AMOC-Climate Linkages in the North and/or South Atlantic (NOAA-OAR-CPO-2016-2004413) to ‘refine the  current scientific understanding of the AMOC state, variability and change’ by focusing on the  interannual and decadal variability for the LS branch. The proposed work contributes to three  priorities identified in the US AMOC 2014 report and advances the NOAA’s Next-Generation  Strategic Plan to ‘improve scientific understanding of the changing climate system by diagnosing  the physical and biogeochemical biases in the CGCMs that are used in the future prediction and  projections by the Intergovernmental Panel on Climate Change’.

Transient tracer fingerprints of Atlantic Meridional Overturning Circulation in Observations and Models

Principal Investigator(s): Anand Gnanadesikan (Johns Hopkins), Thomas Haine (Johns Hopkins), Darryn Waugh (Johns Hopkins)

Year Initially Funded: 2016

Program (s): Climate Variability and Predictability

Competition: AMOC-Climate Linkages in NA/SA

Award Number: NA16OAR4310174 | View Publications on Google Scholar

Transient tracers offer a unique and important window into the Atlantic Meridional Overturning Circulation (AMOC). While they have been used to estimate the total rate of formation of NADW and to trace the pathways by which watermasses spread, less attention has been paid to the ways in which tracers can tell us about how the overturning is changing. Increasing amounts of observational data and model simulations with transient tracers offer new opportunities to understand the relationship between transient tracers and the large-scale ocean circulation. This proposal has three parts:
1. Analysis of coupled climate models: As demonstrated in this proposal, transient tracers in climate models can be better correlated with long-period variability in the overturning than spot measurements of the overturning itself. However, the fingerprint of overturning variability in tracers such as ideal age and oxygen has a complex three-dimensional spatial structure, with different responses at different latitudes. We propose to conduct a cross-model comparison of different models developed at NOAA GFDL as well as Earth System Models that are part of the IPCC AR5 model intercomparison. We will examine how robust the correlations with overturning are across models and evaluate how much data is required to extract them from other modes of variability. Anand Gnanadesikan will lead this part of the project.
2. Observational data analysis: We will analyze repeat hydrographic sections (Line W between Cape Cod and Bermuda, and elsewhere) to examine whether the patterns of changes in ventilation age and oxygen seen in the models also show up in the observations. We will also explore whether transient tracers in the observations are linked to changes in the stratification, as they appear to be in the models. If found, robust relationships between oxygen, stratification and age would enable a much broader mapping of changes in ventilation pathways within the North Atlantic. Darryn Waugh will take the lead in this part of the project.
3. High-resolution modeling. In order to better understanding the sources of the tracer fingerprints we see in the coupled models, we propose to conduct some high-resolution (1/10 degree) regional simulations of the North Atlantic in which different forcings are applied to change the overturning circulation and the resulting fingerprints on the tracer fields are computed. Insofar as we see the same results as the lower-resolution simulations, these results will help establish the robustness of tracer signatures of overturning change. If they show very different results, this will point to the importance of eddy processes in setting up tracer anomalies. Thomas Haine will lead this part of the project.
Key products that will emerge from this work are maps of mean age change over the North Atlantic over time and hopefully, identification of fingerprints of AMOC variability on the age and oxygen fields. This could allow extension of our estimates of AMOC variability back in time, which would also help to constrain mechanisms for AMOC variability.

The Southward Returning Pathways of the AMOC and Their Impacts on Global Sea Surface Temperature

Principal Investigator(s): Chunzai Wang (NOAA/AOML), Sang-Ki Lee (NOAA/AOML), Marlos Goes (NOAA/AOML)

Year Initially Funded: 2016

Program (s): Climate Variability and Predictability

Competition: AMOC-Climate Linkages in NA/SA

Award Number: | View Publications on Google Scholar

Our recent study showed that there exists a coherent spatial pattern of inter-hemispheric global model sea surface temperature (SST) biases in CMIP5 (Coupled Model Intercomparison Project phase 5) climate models and this global pattern of model SST biases is closely linked to the strength of simulated Atlantic Meridional Overturning Circulation (AMOC). The models with a weaker AMOC are associated with cold SST biases in the entire Northern Hemisphere, and with an anomalous atmospheric pattern that resembles the Northern Hemisphere annular mode. These models are also associated with a strengthening of Antarctic Bottom Water (AABW) formation and warm SST biases in the Southern Ocean. However, in many of these models, the amplitudes of the AMOC agree very well with or are even larger than the observed value of about 18 Sv at 26.5°N, but they still show cold SST biases in the Northern Hemisphere. This suggests that the AMOC strength may not be the only factor that causes the cold SST bias. A common symptom in these models is that the returning flow of the AMOC at depth is too shallow. A shallow returning flow would carry excessive heat southward; thus the net northward heat transport by the AMOC would be weaker than the observed. The shallow returning flow in CMIP5 models should be linked to the bias in the southward pathways of the AMOC at depth. We propose to continue our investigations to (1) diagnose the meridional heat transport and its link to model SST biases in CMIP5 models, (2) perform and analyze “robust diagnostic” simulations of the AMOC to reconstruct realistic southward returning flow pathways of the AMOC, (3) explore AMOC southward returning flow pathways and sources of the shallow returning flow of the AMOC in CMIP5 models, (4) investigate the relationship of North and South Atlantic water masses associated with the AMOC, and (5) examine the impacts of improved AMOC on global SST. We will use available hydrographic observations interpolated into isopycnal surfaces, CMIP5 outputs, and model experiments of NCAR Community Earth System Model and an intermediate complexity model.
The proposed work directly contributes to the priority for NOAA FY2016 CPO/CVP funding: “Solicits projects that will refine the current scientific understanding of the AMOC state, variability, and change. Specifically, projects are sought that use newly deployed and existing observations in combination with modeling experiments to refine our understanding of the present and historical circulation (and related transports of heat and freshwater) in the North and/or South Atlantic. An emerging priority is to provide a more detailed characterization of AMOC flow pathways and their impact on variability.” The main outcome of this study will greatly improve our understanding of the decadal predictability of the AMOC and associated climate impacts, and help improve CMIP5 models.

Towards an Improved Understanding of the Initiation and Propagation of the Madden-Julian Oscillation

Principal Investigator(s): Xianan Jiang, UCLA JIFRESSE; Ming Zhao, GFDL/NOAA

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Climate Process Teams ΓÇô Understanding MJO Initiation and PropagationYear

Award Number: NA15OAR4310177 | View Publications on Google Scholar

The Madden-Julian Oscillation (MJO) exerts significant influences on global climate and weather, and serves as a critical basis of the “Seamless Prediction” concept by bridging the forecasting gap between medium- to long-range weather forecasts and short-term climate prediction. However, our understanding of the essential MJO physics is still elusive. The MJO remains poorly represented in current climate models, which leaves us greatly disadvantaged in undertaking climate change studies, particularly in projecting future changes in extreme events that are significantly modulated by the MJO.

Motivated by exciting recent developments in MJO observations (the DYNAMO field campaign), modeling (the MJO Task Force/GEWEX GASS MJO Inter-comparison Project), and theories (e.g., the “moisture mode”), and by taking advantage of the availability of these unprecedented datasets, we propose to form a climate process team to expedite investigations on key physical processes responsible for initiation and propagation of the MJO. This team is built upon strong expertise in MJO studies among research groups from UCLA/JPL (Jiang and Waliser, co-organizers of the MJO Task Force/GEWEX GASS global MJO evaluation project, with expertise in observational and modeling diagnosis and MJO sciences), GFDL (Zhao and Lin, members of the core GFDL model development team, with expertise in model development), UH (Wang, with expertise in MJO theories) and CSU (Johnson, one of the lead PI of the DYNAMO field campaign, with expertise in in-situ observations). The proposed work will entail observational studies, in particular by utilizing the DYNAMO in-situ and satellite observations, 27 climate model datasets from the MJO Task Force/GASS MJO Project especially with the unique model output of physical tendency terms, as well as extensive experiments based on a newly developed state-ofthe- art GCM at the NOAA GFDL (HIRAM3.5) which exhibits superior MJO skill. We will thoroughly investigate critical physical processes for the MJO instability and propagation, with a primary focus on feedbacks between environmental moisture and convection, convection and its induced circulation, and cloud-induced radiative heating and convection. This study will significantly promote our understanding of the key model physics for realistic MJO simulations, thus leads to reduction of model biases in representing the MJO, which provides a major source of global predictability on the sub-seasonal time scale.

This proposal is strongly relevant to one of the NOAA NGSP’s long-term goal, “toward an improved scientific understanding of the changing climate system”, by advancing core capabilities in “understanding and modeling” and “predictions and projections”, as well as societal challenges in “climate impacts on water resources” and “changes in extremes of weather and climate”. In particular, this research directly addresses CVP program’s FY2015 calls for “Understanding Processes Affecting Madden-Julian Oscillation Initiation and Propagation” through a climate process team with expertise on observational diagnoses, theoretical understanding, modeling of the MJO. This proposed study is also in concert with one of the main goals of the WWRP/WCRP’s recently launched Sub-seasonal to Seasonal prediction (S2S) initiative, “to improve forecast skill and understanding of the sub-seasonal to seasonal timescale”, and greatly contributes to efforts in MJO process-oriented diagnoses led by the WGNE MJO Task Force.

Improvement of MJO simulation in NCEP Coupled Forecast System: Upper ocean and air-sea coupled processes

Principal Investigator(s): Toshiaki Shinoda, Texas A&M; Alexander Soloviev, Nova Southeastern University; Wanqiu Wang, NOAA/NCEP; Ren-Chieh Lien, University of Washington; Joshua Fu, University of Hawaii; Hyodae Seo, WHOI

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Climate Process Teams ΓÇô Understanding MJO Initiation and Propagation

Award Number: NA15OAR4310173 OR NA15OAR4310174 OR NA15OAR4310175 OR NA15OAR4310176 | View Publications on Google Scholar

Accurate simulation and prediction of the Madden-Julian Oscillation (MJO) is one of the major challenges for climate modeling and operational weather forecasts. The MJO in the NCEP Coupled Forecast System (CFS) is too weak and propagates too slowly, particularly during its initiation and evolution over the Indian Ocean. With the objective to advance our understanding of the MJO initiation processes and improve MJO prediction, DYNAMO international field campaign provides a substantial amount of oceanic and atmospheric in-situ data. In the last few years, the DYNAMO data have been used to identify important oceanic, atmospheric, and air-sea coupled processes in the MJO initiation and propagation. A primary goal of this proposed study is to advance MJO simulation and prediction in NOAA CFS by improving the representation of the air-sea flux and upper-ocean vertical mixing. The DYNAMO data and the outcome from our previous DYNAMO projects will be maximally utilized for the improvement of MJO simulations. To accomplish this goal, we propose to:

(1) Improve the one-dimensional General Ocean Turbulence Model (GOTM) by including a new mixing scheme developed by Soloviev et al. (2001) that has realistic performance in the tropics and extra-tropics in capturing large diurnal warming and responding to strong westerly wind bursts. The improved GOTM will be tested at DYNAMO field observation sites where accurate surface fluxes as well as high quality upper ocean data are available. These schemes will be further tested in the uncoupled ocean component of CFS with an enhanced vertical resolution.
(2) Develop computationally efficient surface flux algorithm using the most updated version of TOGA COARE bulk flux algorithm, in-situ flux observations, and the method used by Kara et al. (2000, 2005). The algorithms will be carefully validated against DYNAMO observations and tested in the atmospheric component of CFS.
(3) Implement the improved ocean mixing parameterization and air-sea flux algorithm in coupled CFS, and evaluate the MJO simulation and prediction skill based on the comparison with a variety of in-situ and satellite observations, and regional coupled model experiments.

We anticipate that the proposed research with the improved CFS will result in a significant improvement in the forecast of subseasonal variability including the MJO and associated variability such as tropical storms and North America weather. The schemes developed, tested, and implemented in the project also provide guidance for improving the next generation CFS and other coupled models in the climate community, which generally have poor representation of the upper ocean processes and deficient surface fluxes critical to the simulation of the MJO.

Advancing understanding of sea ice predictability with sea ice data assimilation in a fully-coupled model with improved region-scale metrics

Principal Investigator(s): Cecilia Bitz, University of Washington; Adrian Raftery, University of Washington

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310161 | View Publications on Google Scholar

Predictions of sea ice on subseasonal to interannual timescales has the potential to be of widespread value if they are skillful at the lead times and spatial scales needed by forecast users. Understanding sea ice predictability is needed for high-stakes decision-making, such as arises in shipping, accessing resources, and protecting Arctic communities. Current prediction efforts have focused mainly on predicting total northern hemisphere sea ice extent (SIE), termed pan-Arctic SIE. To succeed at predicting regional scales requires significant new effort in three key areas. First, data assimilation techniques must be advanced to accurately initialize sea ice and other components at proper spatial scales. Second, metrics are needed to quantify the skill at the relevant spatial scales and for patterns of interest. Identifying key metrics is motivated by the expectation that a forecast system can't be improved without first developing adequate metrics for evaluating the features of importance. And third, effective statistical post processing methods are needed to correct for systematic biases in the resulting forecasts and to compute forecast probability.

We propose to investigate methods and develop the tools needed to address these three issues in building successful forecast systems. We propose to conduct our research in a well- studied, state-of-the-art sea ice component that is part of a global climate model. To turn this global model into a premier sea ice forecast system, we will work with Jeffrey Anderson and Nancy Collins and the NCAR Data Assimilation Research Testbed to implement DART to assimilate sea ice observations.

With this data assimilating forecast system, we will develop new evaluation metrics to investigate which observations are most essential among in situ measurements (including buoy and ship-based data) and remote sensing. We plan to investigate which regions are most predictable and what mechanisms (including mechanisms that involve coupling between ice, ocean and atmosphere) are responsible. Another important part of our project is to compare predictability in our system to others. We will undertake this with our links to the Sea Ice Outlook project and by providing our research on new metrics to evaluate regional patterns to other modeling centers for detailed intercomparisons. We have plans to collaborate directly with Rym Msadek and colleagues at GFDL to undertake a detailed comparison between the two premier U.S. global climate models, which have the most advanced sea ice components and high fidelity in the Arctic Ocean and atmosphere simulations. We also have discussed collaborating with Pablo Clemente-Colon, Chief Scientists at the National Ice Center, to better address sea ice forecast users needs in the metrics of local and regional-scale sea ice that we develop.

Our project has direct relevance to NOAA CVP Competition by exploring the value of assimilating sea ice observations, developing metrics that evaluate spatial distributions relevant to sea ice, and investigating mechanisms of regional sea ice variations. Our project is aligned with NOAAs goal of improving future operational predictions on time scales of a few months to decades. Our system will be capable of informing future data acquisition.

Seasonal to interannual variability and predictability of Arctic summertime sea ice associated with tropically forced planetary wave patterns

Principal Investigator(s): Qinghua Ding & Axel Schweiger & David Battisti, University of Washington; Michelle L'Heureux & Qin Zhang, NOAA/NCEP

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310162 | View Publications on Google Scholar

Increases in economic, environmental, and security interests in the Arctic demand improved prediction capabilities. The proposed project will explore a new path towards improved predictions of Arctic sea ice. We will investigate how teleconnections between tropical sea surface temperatures (SST) and high latitude circulation patterns can be exploited for sea ice predictions. Recent climate change in the Arctic is generally attributed to anthropogenic drivers and related feedbacks between sea ice, the ocean, and the atmosphere. However, work by Ding et al. (2014) and others (e.g. Trenberth et al. 2014) suggest that tropical Pacific SST variability is important in modulating recent Arctic climate variability by influencing the high-latitude atmospheric circulation. So far, these papers have examined the teleconnection between tropical SSTs and Arctic circulation and surface air temperatures. One unresolved question is how much does this tropical-Arctic teleconnection affect sea ice variability and predictability? This proposal aims to fill that gap. Indeed, preliminary results offered in this proposal suggest that these links with sea ice exist. The proposed work will focus on the implications of this link for seasonal predictability of sea ice and explore how a hierarchy of models captures this link and can be used and improved to enhance Arctic sea ice prediction.

The main source of sea ice predictability in current state-of-the-art models and statistical forecasts originates from the long-term trend of sea ice. On seasonal and interannual time scales, skillful prediction is related to initial sea ice thickness and the oceanic state. Less attention has been given to sea ice predictability arising from teleconnection in the atmosphere which might connect sea ice to ocean states elsewhere. In this project, we hypothesize that there are states of the high latitude atmospheric circulation that are predictable and that their impact on Arctic summer sea ice can be used to improve sea ice predictions. Specifically, our working hypothesis is that the sea ice state at the sea ice minimum (September) is predictable because it is tied to preceding summer Northern Hemisphere circulation patterns which in turn depend on tropical SSTs. If forecast models can replicate this tropical influence on sea ice, a gain in forecast skill can be expected. Whether or not this hypothesized link is robust and replicated in current models needs to be thoroughly examined and is the primary goal of the proposed work. We pursue these goals by executing the following tasks: We will (1) explore the statistical linkage between tropical SSTs-related, high-latitude circulation, and observed Arctic sea ice; (2) examine the dynamical and thermodynamical mechanisms that contribute to the observed connection between the tropically driven high-latitude circulation variability and sea ice by using the PIOMAS and the NCAR CESM1.2; (3) evaluate the performance of CFSv2 and NMME models in reproducing the tropical–sea-ice teleconnection over the last 30 years, which includes a diagnosis of successes and failures and an assessment of the associated sea ice prediction skill; (4) transfer our knowledge for incorporation into operational predictions within NOAA.

This proposal targets all three priorities of the CVP program solicitation by focusing on mechanisms of tropical-extratropical interaction affecting Arctic sea ice during summer and their relevance to the operational NCEP CFSv2 and NMME models. Moreover, all activities are strictly related to the objectives of the NOAA Next-Generation Strategic Plan of “improving scientific understanding of the changing climate system and its impacts.” The project combines the PIs’ expertise in large-scale climate dynamics (atmosphere-ocean-sea ice interaction) with experience using global climate models, regional sea ice-ocean models, and NOAA’s operational models and products that offer forecaster decision support and assist in real-time monitoring.

Oceanographic controls on Arctic sea ice and its future evolution

Principal Investigator(s): Anand Gnanadesikan & Thomas Haine, Johns Hopkins University

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310172 | View Publications on Google Scholar

The annual cycle of sea ice in the Arctic and marginal ice zones is strongly affected by the flux of heat from the ocean to sea ice. This flux is mediated by a number of processes:

1. During the summer, solar radiation can penetrate below the seasonal mixed layer. This is mediated by colored dissolved organic material, whose concentration is thought to be increasing in the Arctic, and by the presence of clear ice-melt layers. Neither of these processes is well-represented in the current generation of GFDL coupled climate models.
2. During the winter, this heat can be returned to the mixed layer by mixing, and additional heat is added from Atlantic waters entering the Arctic. The ease with which this occurs depends on the amount of freshwater stored in the mixed layer and the depth to which this water is mixed. Understanding the evolution of freshwater anomalies within the Arctic may therefore be important for predicting the future of Arctic sea ice.
3. Once it reaches the mixed layer, the heat must be transferred to the sea ice by turbulent exchange. The current version of the GFDL model parameterizes this exchange in a relatively crude fashion, using a heat transfer coefficient that is independent of the friction velocity.
Our proposal will carry on work currently being done at Johns Hopkins to look at all three processes.

Graduate student Grace Kim has recently developed a new parameterization of solar absorption which includes colored dissolved material (CDM), an important absorber of light in the open-ocean Arctic waters, and implemented this within the GFDL CM2Mc model (Galbraith et al., 2011). She finds that the inclusion of CDM produces significant regional changes in Arctic ice cover with a small overall increase. It is thus possible that increasing CDM in Arctic rivers and increasing chlorophyll in the Arctic interior will serve as a negative feedback on sea ice loss. We propose to expand this work to higher resolution GFDL models, particularly ESM2G, which has a very different mixed layer scheme.

Tom Haine's group has worked extensively on the processes maintaining the freshwater anomalies in the Arctic. A particular question we wish to examine is whether changes in Arctic freshwater storage will modulate both the seasonal storage of heat and the supply of heat from warm Atlantic waters. This work will be done using high-resolution models of the Arctic previously used in Haine's group. Postdoctoral support will be requested for this task.

Graduate student Eshwan Ramadu in Gnanadesikan's group has been examining the impact of relaxing the assumption of a constant (relatively large) heat transfer coefficient within the GFDL ice model. This work has found that increasing this coefficient (as might be expected to occur with thinner sea ice) leads to a build up of freshwater in the Beaufort Sea and a reduction of sea ice in the marginal ice zone. We have also started simulations where the ice-ocean heat transfer coefficient is replaced with one that is dependent on the friction velocity. Support is requested to continue this work.

We anticipate that all of these projects will be conducted in collaboration with Gnanadesikan's former colleagues at the Geophysical Fluid Dynamics Lab, particularly Bob Hallberg and John Dunne.

Improving seasonal predictability and prediction of Arctic sea ice and associated feedbacks on mid- and high-latitude climate in CFSv2

Principal Investigator(s): Jiping Liu, SUNY at Albany; Xingren Wu & Robert Grumbine, NOAA/NCEP

Year Initially Funded: 2015

Program (s): Climate Variability and Predictability

Competition: Understanding Arctic Sea Ice Mechanisms and Predictability

Award Number: NA15OAR4310163 | View Publications on Google Scholar

Recent changes in the extent, thickness, and properties of Arctic sea ice have captured attention and posed significant challenges to a wide range of stakeholders. There is a rising demand for sea ice prediction at seasonal-to-interannual timescales. Sea ice prediction is challenging in the context of climate prediction models. Relative to the NCEP Climate Forecast System version 1 (CFSv1), one of the most important developments in the CFSv2 is the incorporation of a sea ice model component. Our evaluations suggested that although the CFSv2 captures the observed seasonal cycle and trend of Arctic sea ice to some extent, large errors exist. The most significant biases are sea ice too thick with interannual variability that is too weak. A major cause of the bias is lack of observations of sea ice thickness over broad areas of the Arctic that would aid in the forecast procedures. Another potential cause of the bias is that assumptions of parameterizations of sea ice optical properties currently made in the sea ice model component of the CFSv2 are inadequate to accurately simulate radiative interactions among atmosphere, sea ice and ocean as Arctic sea ice entering a new regime of thinner and predominantly first-year ice.

This project targets to advance understanding of Arctic sea ice interactions, enhancing seasonal predictability and prediction of Arctic sea ice, and northern mid- and high-latitude winter climate associated with rapid changes of Arctic sea ice in the CFSv2. This serves as an important incremental step toward achieving improved operational prediction system. The proposed work not only enhances seasonal sea ice predictions for the existing operational CFSv2, but can also be applied to the development of the next generation of the NCEP Climate Forecast System, which will include various upgrades. The following targeted activities provide a framework for our project:
1) Assimilate the newly available satellite-based sea ice thickness in the Arctic using a local singular evolutive interpolated Kalman filter, which provides initial conditions for the CFSv2.
2) Incorporate a prognostic model of melt ponds in the sea ice model component of the CFSv2, which allows for changing pond conditions, with implications for the ice-albedo feedback.
3) Implement a more incremental modification of the existing radiative transfer scheme used in the sea ice model component of the CFSv2, and integrate it with the melt pond model.
4) Conduct hindcasts/forecasts with multi ensemble members for 2003-2015, and investigate impacts of the assimilation of observed sea ice thickness, improved sea ice optical parameterizations, and the use of the latest global forecast system on sea ice predictions.
5) Analyze overall skill in forecasting sea ice, the capability in capturing the observed intraseasonal-to-interannual variations of sea ice, and the predictability of sea ice and its relationship with the internal variability in the fully coupled forecast system.
6) Investigate impacts of improved sea ice predictions on the overall skill in forecasting winter climate (including extremes) over northern mid- and high-latitudes in the CFSv2.

This project directly addresses the FY15 CVP Arctic focus to “develop a capability to skillfully and reliably predict variations and changes in Arctic sea ice on time scales of a few months to decades to improve our predictive capability and address the need for environmental information for informed decision making.” The proposed work is highly relevant to the goal of this competition “improve future operation predictions”, and leverages scientific advances by the research community external to NOAA’s operational climate centers and seeks to test and evaluate the potential of experimental models and analysis for operational use.

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