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Interaction of the Lower Atmosphere and Upper Ocean

Principal Investigator(s): James C. McWilliams (UCLA); Peter P. Sullivan (NCAR), Lionel Renault (UCLA)

Year Initially Funded: 2019

Program (s): Climate Variability & Predictability

Competition: Observing and Understanding Upper - Ocean Processes and Shallow Convection in the Tropical Atlantic Ocean

Award Number: NA19OAR4310377, NA19OAR4310378 | View Publications on Google Scholar

The proposed research is a joint project between UCLA and NCAR. The research is for process modeling of fine-scale circulations in the lower atmosphere and upper ocean in the northwest Tropical Atlantic as part of the U.S. Atlantic Trade-wind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) and the European EUERC4A-OA Projects. It is in response to NOAA's Climate Variability and Predictability (CVP) Program: Competition 2: CVP-Observing and Understanding Upper-Ocean Processes and Shallow Convection in the Tropical Atlantic Ocean. The guiding hypothesis of the research is that surface heterogeneities in oceanic temperature (SST) and currents induce heterogeneities in the air-sea fluxes of heat, moisture, and momentum, which in turn modulate the mesoscale and submesoscale circulations in the oceanic surface layer and atmospheric boundary layer. The source of the heterogeneity is oceanic mesoscale eddies and submesoscale fronts. We will use two modeling approaches to elucidate the interaction between the lower atmosphere and the upper ocean: idealized flow configurations in a Large Eddy Simulations (LESs) that resolve the boundary-layer turbulence (led by NCAR) and “realistic” down-scaled coupled simulations using the Weather Research and Forecast (WRF) and the Regional Oceanic Modeling System (ROMS) with parameterized vertical fluxes due to boundary-layer turbulence (led by UCLA). The phenomena arising in these separate, different-scale simulations will be used to inform each other to develop, by bootstrapping, a better process understanding across the interacting range of scales from boundary-layer turbulence to the mesoscale winds and currents. We will design a sequence of studies that explore, in the context of Tropical Atlantic phenomena, how submeoscacle currents interact with the boundary layer turbulence in the ocean, how surface gradients in SST and currents interact with the boundary layer turbulence in the atmosphere, how the resulting secondary circulations extend vertically through the upper ocean and lower atmosphere, and how the Thermal and Current Feedbacks develop mesoscale and submesoscale correlations across the air-sea interface, even reaching into the shallow cloud layer above. The key methodologies are the massively parallel LES code developed over many years at NCAR, including surface wave dynamical influences in both the air and water, and the ROMS circulation model developed at UCLA that also includes surface wave interactions and allows multiple levels of grid nesting conveying larger scale influences down to finer scale circulations, in particular allowing very high resolution studies of submesoscale phenomena shaped by the encompassing mesoscale eddies and regional currents. Because of the extensive international field measurements planned, we would work closely with the observing groups, especially those that have fine-scale sampling in both time and at least one horizontal coordinate. The intent is to combine the relatively more complete information from model simulations with the measured reality, for the better interpretation of both, and to establish the importance of surface heterogeneity in climate outcomes. To this end we intend to work closely with both the European and American experimental teams. This research enhances our process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists and society can better anticipate the impacts of future climate variability and change.

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

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

Year Initially Funded: 2008

Program (s): Climate Variability and Predictability


Award Number: | View Publications on Google Scholar

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

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

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

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

Year Initially Funded: 2007

Program (s): Climate Variability and Predictability


Award Number: | View Publications on Google Scholar

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

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

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

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


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.

The Role of Ocean Stratification in the Propagation of Intraseasonal Oscillations

Principal Investigator(s): Janet Sprintall (Scripps)

Year Initially Funded: 2017

Program (s): Climate Variability & Predictability

Competition: Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region

Award Number: NA17OAR4310257 | View Publications on Google Scholar

Intraseasonal Madden-Julian Oscillation (MJO) atmospheric forcing exerts a profound influence on the near ocean surface layer of the tropics through the coupled air-sea system that in turn affects the structure, development and propagation of the mesoscale convective systems that are part of the MJO. Models suggest that an accurate depiction of the upper ocean stratification in the MC is necessary to correctly reproduce intraseasonal variability. Nonetheless, the dynamics and time scales of the processes and characteristics at the air-sea interface during MJO events are still not well understood. One large gap in our understanding is the role of upper ocean salinity in MJO variability. Since salinity controls stratification throughout much of the MC it can play a critical role in the complex coupling of the air-sea system during MJO events. In many MC regions, a salt-stratified but isothermal “barrier layer” can exist that traps fluxes of heat, freshwater, and momentum to a thin surface layer. Climatological variations in the thickness of the barrier layer during MJO events are known to drive SST anomalies that influence the coupled air-sea system. Similarly, few studies of the diurnal-intraseasonal interaction within the MC have considered the role played by salinity in setting the diurnal ocean stratification. Yet the global maximum in sea surface salinity diurnal amplitude lies within the MC region. The main aim of the proposed effort is to understand the mechanisms responsible for upper ocean stratification variability in the MC, with a particular attention on near surface salinity stratification and how this influences the structure and propagation of MJO convection and winds. High-resolution ship-board measurements of the upper ocean temperature and salinity will be obtained using a portable underway CTD (uCTD) system. The data will provide distinct case studies of the ocean conditions during MJO events, that will be examined in concert with remotely-sensed and other in situ datasets, with science objectives to (1) determine the characteristics and the time and space scales of upper ocean salinity variability of importance to MJO variability; (2) identify the main forcing mechanisms that control that salinity variability; and (3) establish connection with the intraseasonal MJO atmospheric phenomena and relationship to the propagation characteristics (speed, intensity, MJO phase, geographical location etc) of the MJO across the MC region. Relevance to Competition: The proposed research is a contribution to the CVP - Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region. The MJO is the dominant mode of intraseasonal variability in the global tropics influencing the monsoon systems, convective patterns, cyclogenesis and triggering of El Niño events. The MC plays a special role in the behavior of the MJO, critically affecting the propagation speed and intensity of the convective systems in ways that are not yet fully understood. The proposed research aims to provide new information on the processes that control upper ocean stratification in the MC region and so enable a better representation and prediction in models of the characteristics, structure and evolution of the MJO propagation in the MC. The project directly addresses the key NOAA long-term goal of improving scientific understanding of the Earth’s climate variability.

Developing PSSdb: a Pelagic Size Structure database to support biogeochemical model development

Principal Investigator(s): Jessica Luo (NOAA/GFDL), Rainer Kiko (Sorbonne Université)

Year Initially Funded: 2021

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: GC21-407a, NA21OAR4310254 | View Publications on Google Scholar

Marine plankton are essential components of ocean ecosystems, forming the bottom of the food chain and serving as controls on large-scale biogeographic patterns in ocean carbon, nutrients, and oxygen. Earth System Model (ESM) projections suggest that ocean warming and stratification will drive decreases in net primary production (NPP) and shifts in plankton community composition and size structure. These changes to the plankton community have subsequent implications for decreasing the strength of the biological pump, as well as declining fisheries productivity through trophic amplification mechanisms. However, a critical underlying factor driving these shifts -- the simulated size structure of plankton and particles -- is difficult to validate, as global datasets are lacking. Fortunately, new data streams from plankton imaging systems are capable of providing 3-dimensional, broad-spectrum views on plankton and particle size spectra, provided data are properly harmonized and cross-calibrated to a single standard. Following the structure of the World Ocean Database (WOD) and COPEPOD database, we propose to establish a methodological processing pipeline for the ingestion, calibration, and harmonization of imaging data on plankton and particles spanning five orders of magnitude (1 micron - 10 cm). The ultimate goal of our work is the development of the Pelagic Size Structure database, or PSSdb, as a resource for global gridded data. We will start off with data from five imaging systems that include in-situ samplers to tabletop imagers: Imaging FlowCytoBot (IFCB), FlowCam, Underwater Vision Profiler (UVP), ZooScan, and In-situ Ichthyoplankton Imaging System (ISIIS). The resultant data will be structured in four levels, ranging from low (level 1) to high (level 3) taxonomic resolution, and will be made publicly available through NOAA websites. Our methodological framework and resultant database will be broadly extensible, and able to incorporate both historical data and future sampling efforts. As plankton imaging systems become increasingly utilized in national and international sampling programs, and are being developed for automated vehicles, drifters, and floats, we anticipate both a growing need for data consolidation and harmonization, as well as the opportunity for these datasets to inform ESM development. Ultimately, an improved understanding of the key ecological mechanisms driving marine ecosystem shifts, elucidated by models with strong empirical validation, will increase confidence in Earth System Model projections and associated links between the climate and fisheries.

Signature of the Atlantic Meridional Overturning Circulation in the North Atlantic Dynamic Sea Level

Principal Investigator(s): Jianjun Yin, University of Arizona

Year Initially Funded: 2013

Program (s): Climate Variability and Predictability


Award Number: | View Publications on Google Scholar

The dynamic sea level (DSL) is closely linked to the Atlantic Meridional Overturning Circulation (AMOC) through the geostrophic balance, and is an important fingerprint of the variability and change of the AMOC. The primary goal of this proposal is to systematically investigate the AMOC-DSL relationship in various model integrations, to utilize the long-term DSL observations to detect decadal to multi-decadal variability and long-term trend of the AMOC, and to assimilate the DSL information into the predictability and decadal prediction studies of the AMOC. To achieve the goal, we will use a set of state-of-the-art ocean and climate models, in particular, a consistent data-model framework developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of NOAA, to investigate the robust and accurate relationship between the AMOC and the DSL in the North Atlantic and along the east coast of the U.S. We will compare and combine the DSL data from the high-quality tide gauge records and satellite altimetry, with various model integrations including ocean climate reanalysis, hindcasts, model control and historical runs, and simulations under different external forcings. The objectives are a) to investigate the different sensitivity of the coastal DSL north and south of Cape Hatteras to the AMOC; b) to identify robust DSL patterns in the ocean interior associated with the variability and change of the AMOC; c) to reconstruct the AMOC during the past century based on the DSL information and the AMOC-DSL relationship; and d) to improve prediction and projection skills of the AMOC, the DSL in the North Atlantic, and regional and global climates.

The project will be accomplished through innovative research and educational activities. Through collaborative work with colleagues at GFDL and the University of Arizona, and interactions with students and the general public, this project will make significant contribution to the scientific community and the education of future climate scientists and the general public. The results from this proposal will be made available to broader ocean, climate variability and change and sea level rise research communities, local governments and policymakers. They will contribute to the Coupled Model Intercomparison Project (CMIP5), the Coordinated Ocean-ice Reference Experiments (CORE), and the model development community at GFDL.

This proposal targets and is closely relevant to the following competition: ESS - Atlantic Meridional Overturning Circulation (AMOC): Mechanisms & Decadal Predictability. The NOAA’s long-term goals, as outlined in NOAA’s Next-Generation Strategic Plan, include improved scientific understanding of the changing climate system and its impacts, and assessments of current and future states of the climate system that identify potential impacts and inform science, service, and stewardship decisions. We anticipate that the outcome of this project will meet NOAA’s goals by deepening our understanding of the long-term variability and change of the AMOC, revealing the robust AMOC-DSL relationship in past, current and future climates, and providing reliable decadal predictions and long-term projections of the AMOC. The proposal will also generate near-term predictions and long-term projections about the AMOC-induced sea level rise along the eastern U.S. coast. The results would be valuable for the coastal communities and policy-makers to design sustainable coastal planning, and therefore are in line with NOAA’s missions (comprehensive ocean and coastal planning and management).

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.

From Boundary Layer to Deep Convection: The Multi-Plume Eddy-Diffusivity/Mass-Flux (EDMF) Fully Unified Parameterization

Principal Investigator(s): Joao Teixeira (UCLA/JPL); Rong Fu and Mikael Witte (UCLA); Georgios Matheou (University of Connecticut); Leo Donner (NOAA/GFDL); Julio Bacmeister (NCAR)

Year Initially Funded: 2019

Program (s): Climate Variability & Predictability

Competition: Climate Process Teams (CPTs) - Translating Ocean and/or Atmospheric Process Understanding to Improve Climate Models

Award Number: GC19-401 | View Publications on Google Scholar

The key objective of this project is to reduce critical systematic biases in the GFDL model related to the boundary layer, convection and clouds by implementing, and evaluating, in the GFDL model, a new fully unified boundary layer and deep convection parameterization based on the multi-plume Eddy-Diffusivity/Mass-Flux (EDMF) approach. Turbulence and convection in the atmosphere are at the core of key climate prediction problems. For example: i) to reduce uncertainties in climate projections, it is essential to improve predictions of cloud feedbacks (how clouds respond to, and influence, climate change), which are controlled by the interactions between a turbulent flow with water phase transitions and radiation; ii) to improve extreme weather prediction for the next few decades as climate changes, it is essential to improve our understanding of how moist convection responds to a warmer world. It is increasingly clear that to realistically represent the different manifestations of turbulence and convection in the atmosphere, new unified parameterizations that consider all types of sub-grid flow in one single scheme, are needed. In this context, a parameterization such as EDMF that unifies boundary layer with moist convection (both shallow and deep) is a promising approach. EDMF is based on the unification of concepts generally used for the parameterization of turbulence in the boundary layer (ED) and of moist convection (MF). Studies have shown the potential of EDMF to represent dry and moist convective boundary layers. In the last few years the Lead PI’s group has developed a new version of EDMF that is particularly well suited to simulate moist convective boundary layers and is able to represent in a realistic manner the dry boundary layer, stratocumulus, shallow and deep cumulus convection. This new version uses a multi-plume approach and the probability density function (PDF) of updraft properties in the surface layer is sampled in a Monte-Carlo manner to start a variety of updraft plumes, with a stochastic lateral entrainment parameterization. The current EDMF version is a turbulence and convection parameterization that can be considered as fully unified, since it is able to represent convective processes from boundary layer convection (dry and with clouds) to deep moist convection. In this project, we will implement and evaluate the new EDMF parameterization in the GFDL model. Initially we will evaluate the new EDMF implemented into the GFDL SCM versus a variety of LES case-studies and results from field experiments. For the full 3D implementation, we will focus our evaluation on cloud and convection variables as observed by satellite instruments during present climate. In particular, we will evaluate how the new EDMF version of the GFDL model is able to simulate key boundary layer and convection transitions such as (i) from stratocumulus, to cumulus and to deep convection (over the tropical and sub-tropical oceans) and (ii) the diurnal cycle of tropical convection over land from a stable boundary layer to dry convection, shallow convection and deep precipitating convection. We will also investigate in detail the impact of the new EDMF GFDL simulations in present and future climate. By developing and implementing a new fully unified boundary layer, cloud and convection parameterization in the GFDL model, and reducing key biases in GFDL’s climate predictions, this project will improve NOAA’s Climate Program Office (CPO) capabilities in Earth system science and modeling, will address CPO’s strategic challenges in the areas of (1) Weather and climate and (2) Climate impacts on water resources, and will ultimately advance the scientific understanding and prediction of climate and its impacts, to enable effective decisions.

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.

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