Modeling, Analysis, and Prediction (MAP) Program

ROSES 15 NASA/MAP Funded Investigations

Research Opportunities in Space and Earth Sciences, 2015 - A.13 Modeling, Analysis, and Prediction  (NNH15ZDA001N-MAP)

NASA's Science Mission Directorate, NASA Headquarters, Washington, DC, has selected proposals for the Modeling, Analysis and Prediction (MAP) Program in support of the Earth Science Division (ESD). The Modeling, Analysis, and Prediction (MAP) program seeks an understanding of the Earth as a complete, dynamic system, with particular emphasis on climate and weather. The MAP program supports observation driven modeling that integrates the research activities in NASA’s Earth Science Program.

The research is distinguished by rigorous examination and incorporation of satellite-based observations, using models to bridge the spatial and temporal scales between satellite observations and observations from ground and air based campaigns. This contributes to the validation of the satellite observations and to observationally based improvements of Earth system model components. MAP strives to generate models and model components that are documented and validated.

The research themes specific to this solicitation include investigations to conduct ocean modeling and data assimilation that incorporates the cryosphere, and continued development of the Earth System Modeling Framework, focusing on its utilization in MAP-supported modeling tools. The ESD has selected 5 out of a total of 8 proposals received in response to this solicitation. The total first-year funding for these four-year investigations is approximately 1.7 million dollars.

Objectives and Significance:
This proposal is a collaboration among University of Maryland and NASA scientists to improve representation of the northern ocean and sea ice within the GEOS earth system reanalysis. We will collect and examine all available ocean and sea ice observations, including new and upcoming data and the new MERRA2 atmospheric reanalysis, and explore their relationships to state variables (e.g. radiances and SST) for the period beginning 1979. One goal is to develop the appropriate statistical relationships (observation operators) required for data assimilation. Experiments will be carried out to incorporate this new data effectively within the current GMAO GEOS integrated ocean data assimilation system (GEOS iODAS). Such experiments will examine the ability of the analysis to represent key features of the historical climate variability of this region. In the second stage of this project we will explore possible upgrade paths for the GEOS iODAS including possible changes to the assimilation system. Among the options we would like to explore is a move to a more fully coupled ensemble Kalman filter-based data assimilation analysis where oceanic observations directly impact the atmospheric model.

The significance of the proposed work lies in its contribution to providing an improved, more consistent analysis of the variable and rapidly changing climate at high northern latitudes within the GEOS framework, increasing the potential of the system to contribute to understanding the roles of the ocean and sea ice systems in climate. The proposal thus directly addresses theme 1.3.1 of the MAP call for proposals, specifically (1) improvements in the Arctic sea ice Ocean in the coupled GEOS5 GCM, and (2) ocean-sea ice state estimation in the Arctic within the GEOS5 framework.

The first part of the proposal relies heavily on statistical analysis of a variety of remotely sensed and in situ data sets, and including output from the iODAS system. A heavy emphasis in this work will be to quantif y uncertainty in the observations as well as in the analysis system. For the second part of the proposal we will be working directly with the ocean model and the coupled system. We will be conducting experiments with data assimilation software. This por tion of the proposal will require significant use of supercomputing resources.

The Earth System Modeling Framework (ESMF) development team will collaborate with NASA GISS, GSFC, and JPL modeling leads to reconcile ESMF usage conventions developed at GSFC, called the Modeling, Analysis and Prediction Layer (MAPL), with the similar National Unified Operational Prediction Capability (NUOPC) Layer developed by operational weather centers and their research partners. Motivating applications will be the sharing of model components between Model E and GEOS-5, and the sharing of model components between NASA centers and NUOPC-based models in the broader community. Key issues to be addressed include 1) how NUOPC-based modeling applications, which currently encompass major physical domains (e.g. atmosphere, ocean, sea ice), can leverage and interact with the deeply nested hierarchies of sub-process components supported by MAPL, and 2) how MAPL-based applications can leverage and interact with NUOPC's advanced capabilities in areas such as grid remapping and the layout of modeling problems on hardware resources. Plans for interfacing MAPL and NUOPC will be developed in the context of desired future capabilities for model infrastructure at NASA, including support for extremely high resolution, scalable model configurations on emerging computer architectures. This work will build on previous collaborations and achievements among the proposing partners.

During the past three decades, satellite observations have recorded significant changes in Arctic and Antarctic sea-ice extent and thickness and in the mass of Greenland and Antarctic ice sheets. These changes have repercussions for regional and global energy balance, weather patterns, ocean circulation, sea level, greenhouse gases, ecosystem dynamics, and human activities. The proposed study aims to make major qualitative and quantitative improvement in the description and prediction of these changes by leveraging and enhancing NASA modeling and data assimilation capabilities developed by the Global Modeling and Assimilation Office (GMAO), the Estimating the Circulation and Climate of the Ocean (ECCO) project, and the Ice Sheet System Model (ISSM). Of particular importance for the proposed study is the Goddard Earth Observing System Model, Version 5 (GEOS-5) and the Modern Era Reanalysis for Research Applications-2 (MERRA2) developed by GMAO; the ocean and sea ice model+adjoint and model-data synthesis capabilities developed by ECCO; and the ice sheet model+adjoint and data assimilation capabilities developed by ISSM.

Although ocean and cryosphere processes are leading candidates for carrying seasonal-to-decadal predictive skill, a major impediment to fully exploiting this predictive skill is a lack of consistency between observations of the Earth System and current-generation climate models. Additionally, there is a lack of feedback between climate model components as a result of assimilation of observations into separate model components. For example, the formal assimilation of observations in atmospheric models results in surface fluxes that produce drifts and biases in the ocean and ice model counterparts. Conversely, surface fluxes that reduce drifts and biases in ocean and sea ice components are not necessarily consistent with the atmospheric model and observations.

The key objectives of the proposed study are to (1) characterize and reduce biases in ocean, sea ice, and ice sheet simulations forced by MERRA2 surface atmospheric boundary conditions, (2) develop a prototype, property-conserving, ocean-ice-atmosphere assimilation system based on the "replay" capability of GEOS-5 in conjunction with the adjoint-model capabilities of ECCO and ISSM, a nd (3) use above data-constrained simulations and forward/adjoint model sensitivity experiments to examine origins and consequences of observed sea ice and ice sheet changes.

The proposed technical steps build on existing capabilities separately developed by GMAO, ECCO, and ISSM. Bringing these pieces together will help shed new light on open scientific challenges pertaining to drivers of polar amplification, pathways influencing observed polar change, and consequent changes regionally and globally. In particular, adjoint and forward model sensitivity experiments of the individual components and coupled system will be used to study causes and consequences of observed thinning and summer retreat of Arctic sea ice, changes in winter sea ice extent around Antarctica, and increasing rates of ice mass loss from Greenland and Antarctic ice sheets.

Consistent with this solicitation's overall vision, the proposed work is a stepping stone towards a next -generation Integrated Earth System Analysis (IESA) that is simultaneously consistent with physics understanding as well as with all available observations. The improved descriptive and predictive skill of this next-generation IESA is expected to provide more precise answers to key NASA Earth Science Research questions, to help shape next-generation multi-platform mission design, and to help support and inform decisions for mitigation and adaptation activities in response to global change.

The Atlantic Meridional Overturning Circulation (AMOC) is a key process that regulates Earth's climate by redistributing heat at the surface and by sequestering heat and CO2 into the deep ocean. Its future changes represent a critical uncertainty of the global climate system's response to anthropogenic forcing, because the underlying processes are not fully understood and not well simulated in the present climate models.

An important driver of the AMOC is the change in the air-sea fluxes (momentum, heat and freshwater) which influence water mass formation in the sub-polar North Atlantic, particularly with regards to high frequency variability such as mid-latitude cyclones (MLCs). Conversely, AMOC affects air-sea fluxes of heat, momentum, freshwater and carbon by controlling the North Atlantic storm tracks, the behavior of the ITCZ, the strength of the uptake of heat and carbon etc, thereby establishing a feedback mechanism that is not yet well explored in the present as well as in future climates. Studies have shown that AMOC maybe weakening [Hakkinen and Rhines, 2004], but others disagree (see discussion in Lozier [2012]).

In this study, we seek a connection between weather (storms) and climate (AMOC) both in observations and models while we assess the NASA-GISS climate model with respect to air -sea fluxes (of heat, momentum, freshwater, carbon and tracer) and overturning.

The objectives of this work are to use observations, reanalysis and model data to:

  1. understand how MLCs influence turbulent fluxes of momentum, heat, freshwater, and carbon, how they affect Labrador Sea Water (LSW) mass formation and CO2 uptake there, and how deep water formation relates to AMOC,
  2. using a process-based evaluation procedure, assess the skill of the GISS climate model in reproducing the "storm regimes" that drive LSW formation and the AMOC, and
  3. given the present model uncertainty, estimate changes in air-sea fluxes, cyclone activity, buoyancy forcing and AMOC in future climate scenarios.

To achieve these goals we will follow a technical approach that consists of utilizing data from multiple sources (satellite, insitu observations, assimilation products), statistical methods to combine data and models, sensitivity experiments, as well as climate modeling. Satellite data from various products and new/improved ocean observations (ARGO, O-SNAP) will provide a coherent picture of both the atmospheric forcing and the high latitude North Atlantic response.The NASA-MAP Climatology of Mid-latitude Storminess (MCMS) methodology will help us identify the storm patterns in both reanalysis and in models. The NASA-GMAO GEOS-5 data assimilation system for both the atmosphere (MERRA) and the ocean will fill in the spatial and temporal scales of the air -sea interaction not covered by observations. A series of experiments with coupled and uncoupled versions of the GISS global climate models will provide model bias estimation and attribution, so that future projections of changes in storminess and AMOC are less uncertain.

With regards to climate modeling at NASA, this work will provide valuable insight towards the development of models with improved skill in seamless prediction, i.e. skill in both the low and the high frequency variability.

The present proposal brings together the GISS and GMAO modeling work, and uses extensively observations to address a key source of uncertainty for present climate and future projections: the relationship between air-sea fluxes and AMOC, which is one of the 2014 NASA Strategic Plan objectives.

With regards to ROSES2015-MAP objectives, this work aims to improve the understanding and representation in the GISS climate model of the atmosphere/ocean interactions (heat, momentum, freshwater, carbon and other tracers) while using a combined ocean/atmosphere data analysis system, that comprises of comprehensive datasets and analysis tools.

Improving our understanding of the variability of terrestrial water cycle components is of critical importance due the impact of water resources on societal applications such as agricultural production, human health, flood, drought, weather and climate prediction. Management of available water resources is widely considered to be one of the most significant current and future global challenges. In the past decades, there have been significant advancements in the development of new modeling tools and techniques, observations of water cycle components from satellites and remote sensing platforms and high performance computing architectures that facilitate simulations at fine spatial and temp oral scales. Computational advances have also made it possible to develop modern data fusion and assimilation methods for extracting the information content of remote sensing observations for hydrological modeling.

The NASA Land Information System (LIS) is such a framework, developed to enable fine scale land surface modeling and the assimilation of terrestrial hydrologic remote sensing observations. LIS features a comprehensive data assimilation subsystem that encapsulates a suite of modeling, computational, data assimilation and inverse modeling tools to facilitate the effective utilization of these modern remote sensing observations. Recent MAP funded efforts have also enabled the use of LIS as the land surface component in the NASA Unified Weather Research and Forecasting (NU-WRF) hydrometeorological system.

Similarly, the Weather Research and Forecasting Model Hydrological modeling extension package (WRF-Hydro) is a new community model coupling framework that encompasses atmosphere and terrestrial hydrology models, designed primarily with the goal of improving hydrometeorological forecasts. WRF-Hydro includes extensive capabilities for modeling terrestrial hydrological processes such as surface and sub-surface runoff, and channel, lake and reservoir flows.

The proposal addresses the research theme 1.3.2, Earth System Modeling Framework for MAP-supported modeling efforts . The integration of LIS and WRF-Hydro will be enabled using the standardized software tools and paradigms identified by Earth System Modeling Framework (ESMF) effort. The proposed system would thus combine the strengths of both LIS and WRF-Hydro and enable the interoperable use of a broader range of computational models in highly scalable environments, employing end-to-end use of standardized data formats. More recently, ESMF has developed a National Unified Operational Prediction Capability (NUOPC) layer, to enable rapid transition and increased interoperability between ESMF-based modeling systems. In this project, we plan to utilize the capabilities of the NUOPC layer to enable the coupled system. The new capabilities will be demonstrated through a series of high-impact case studies involving hydrology missions such as SMAP, GPM and GRACE and coupled applications under the NU-WRF umbrella. The proposed effort is thus directly relevant to the MAP program goals of enhanced observation-driven modeling and data assimilation for improving Earth system model components.