Modeling, Analysis, and Prediction (MAP) Program

ROSES 20 NASA/MAP Funded Investigations

Research Opportunities in Space and Earth Sciences, 2020 - A.16 Modeling, Analysis, and Prediction  (NNH20ZDA001N-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. An overview of the current program may be found at The research themes specific to this solicitation include the representation in Earth system models of clouds, extremes, constituents, and coupling processes, as well as investigation of prediction, predictability and data assimilation.

The ESD has selected 34 out of a total of 175 proposals received in response to this solicitation. The total funding for these investigations is approximately 31 million dollars over four years.

This proposal is concerned with the role of the interaction of aerosol with radiation and clouds in determining the skill and predictablity of subseasonal and seasonal forecasts. It is well established that the emission and transport of particulate matter from natural and anthropogenic sources play a significant role in modulating the Earth’s radiative balance. However, the interaction of aerosol with climate is explicitly described in very few operational forecasting systems.

Since version 2, the NASA subseasonal to seasonal prediction system, GEOS-S2S, is the only near-real-time S2S prediction system participating in the North American Multimodel Ensemble with explicit descriptions of aerosol-cloud and aerosol-radiation interactions (ACI and ARI, respectively), as well as aerosol transport and evolution. However to date no systematic study has been attempted to attribute improvements on the predictability and forecast skill of temperature, precipitation and aerosol optical depth, to the explicit parameterization of ARI and ACI in the GEOS-S2S.

In this proposal we will address these gaps in our understanding of the effects of the complexity of the aerosol representation on S2S forecasting. We will perform a series of long-term reforecasts with varying degree of interaction between aerosols, clouds and radiation, to single out the role of ACI, ARI and aerosol transport on the GEOS-S2S forecast skill and predictability. A novel aspect of this proposal is the exploration of aerosol-related forecast of opportunity (FOO) in S2S prediction. That is, instances of initial conditions that are likely impacted by sufficiently “strong” aerosol events, and where the explicit representation of ACI and ARI in the model physics is expected to enhance S2S predictability. To search for aerosol-related FOO periods, we will develop an aerosol reanalysis with explicit ARI and ACI representations.

Our proposed research takes a deep look at the role of aerosol and clouds in the climate system at the timescale relevant for S2S forecasts. This proposal answers directly to the NASA MAP research focus by improving the representation of processes that would increase predictive skill for precipitation in S2S forecast systems.

Scientific understanding of climate is subject to multiple uncertainties; two of the largest are the roles of aerosols and feedbacks in the climate system. Reducing these uncertainties is an urgent need in Earth System models (ESMs). Agricultural and natural soils are an important source of both nitrogen oxides (NOx) and ammonia (NH3), which are globally important precursors to gas phase chemistry and aerosol formation. Emissions of NOx and NH3 from soils and vegetation are directly dependent on temperature and moisture; state changes in soil moisture can produce regionally important emissions. These climate dependencies can introduce feedbacks in the climate system as a changing climate alters emissions, leading to changing concentrations of aerosols and the short-lived greenhouse gas ozone (O3) that then influence climate. Because most CMIP6-class ESMs rely on prescribed emission input files, they are unable to account for dynamic interactions between climate and soil emissions of NOx and NH3, and to elucidate how transient emission responses to a changing climate influence atmospheric composition and resulting feedbacks to climate. As such, they are also unable to evaluate the implications of these interactions, which are difficult to predict and need to be understood for the development of effective climate and health policies.

Objectives 1-3: Implement new model components for dynamic soil NOx and bi-directional NH3 fluxes in the GISS ModelE3, including new natural NH3 emissions. Objective 4: Evaluate model performance against surface and aircraft observations of gas phase and particulate phase species across the U.S. and satellite column densities from OMI and IASI, with a focus on the midwestern U.S. Corn Belt and the Sahel. Analyses of sensitivity of model bias to scaling factors and biome-specific parameters will be used in model evaluation and optimization.Objective 5: Conduct a series of modeling experiments using the new components, to:- Quantify the contribution of soil emissions to aerosol formation, O3 formation, and the atmosphere’s radiative balance under current climate (5.1) - Diagnose climate-soil feedbacks during historical (5.2, 5.3), and future climate (5.4)- Quantify the impact of soil emissions and climate-soil feedbacks on current and future human health (5.5)

The GISS ModelE has new interactive fire and biogenic—but not soil—emissions. We will implement and evaluate dynamic soil gas exchange schemes for NOx and NH3 in the GISS ModelE3 and use these schemes to understand the complex interactions and feedbacks between a changing climate, soil emissions, and aerosol and O3 formation and forcing, as well as their implications for human health. Specifically, we will pursue the following objectives:

In combination with ModelE’s new dynamic biogenic and fire emission components, this project will allow us to evaluate the effects of a changing climate on terrestrial emissions and their impacts on aerosols, O3, and radiative forcing for the first time in ModelE. It directly addresses the “Constituents in the Earth System” research theme of the MAP program element, multiple questions from the 2017 Decadal Survey, and furthers the program’s core modeling efforts. Our team is uniquely qualified for this project. Hickman has 15 years of experience in soil fluxes and their influence on atmospheric composition using surface measurements, satellite observations, and models. Bauer and Tsigaridis have a combined 30+ years of experience as ModelE developers and are responsible for the atmospheric composition and aerosol components of ModelE, including the influence of soil emissions on aerosol formation.

When subjected to "external"forcings, such as anthropogenic changes in atmospheric greenhouse gases, the atmosphere and surface warm at a rate determined not only by the forcing itself, but also by positive and negative feedbacks: changes in the climate system in response to the forcing that respectively amplify and reduce the warming. Of all the feedbacks, the most uncertain are those associated with changes in clouds in both the current and previous generations of results submitted to the Climate Model Intercomparison Project (CMIP; Bony and Dufresne, 2005; Zelinka et al., 2016, 2020). More specifically, low-cloud feedbacks continue to be particularly challenging for two reasons.

First, the multi-model spread of tropical low-cloud feedbacks dominates the uncertainty in model estimates of equilibrium climate sensitivity (ECS) (Caldwell et al., 2018; Vial et al., 2013), the globally averaged surface air warming resulting from a doubling of atmospheric CO2. Recent global-scale observational studies (Cesana et al., 2019; Cesana and Del Genio, 2020) support previous findings from large-eddy simulations (Bretherton, 2015) and ground-based studies (Nuijens et al., 2015a) that stratocumulus (Sc) and cumulus (Cu) clouds exhibit different feedbacks to global-warming like environmental conditions. The sensitivity of Sc and Cu to cloud-controlling factors as well as their relative presence in the tropics must be realistic in order to produce a plausible feedback under global warming (Cesana and Del Genio, 2020).

Second, low clouds also produce substantial feedbacks in the extra-tropics, which can be either positive or negative, in response to changes in their amount and microphysical properties (Tsushima et al., 2006; Zelinka et al., 2016, 2020). Furthermore, most of the increase in ECS between CMIP5 and CMIP6 models have been linked to extra-tropical low-cloud feedbacks (Zelinka et al., 2020). While the mechanisms involved remain unclear, past studies showed that these feedbacks depend on the sensitivity of cloud phase and cloud opacity to climate warming (Ceppi et al., 2016; Terai et al., 2016), which are largely unconstrained in the current generation of general circulation models (GCMs; Zelinka et al., 2020).

In the proposed work, we focus on improving our understanding of these low-cloud feedbacks and their representation in the NASA Goddard Institute for Space Studies Earth System Model (NASA-GISS ESM). Our strategy complements traditional direct analyses of feedbacks by employing a process-level approach using satellite, ground-based and reanalysis data. To this end, we will apply our framework to four different configurations of the atmospheric component of ModelE3 (the most recent version of the NASA-GISS ESM; Cesana et al., 2019a), which span a diversity of ECS and cloud sensitivities to cloud-controlling factors through different choices of tuning parameters and cloud scheme formulations.

We will assess which parameters affect the feedbacks the most and which combination of parameters best compares with our observational constraints. Building on our results and supplemented by the existing literature, we will construct an additional version of ModelE3 (by modifying the set of tuning parameters and the interactions between the parameterizations of turbulence, convection, and stratiform clouds) that generate the most realistic low-cloud feedbacks. Ultimately, we aim to determine the impact of an improved representation of low-cloud feedbacks on ECS in ModelE3, obtained via improved observational constraints on moist atmospheric physical processes. By doing so, we intend to advance understanding of the cloud-climate feedback mechanisms related to low clouds.

The importance of an Integrated Earth System Analysis (IESA) was highlighted in the 2017 Decadal Survey as being “the future of reanalysis”, and it is one of the long-term goal of the MAP program. Over the past decade, the Global Modeling and Analysis Office (GMAO) at NASA Goddard Space Flight Center (GSFC) has successfully produced high-quality reanalyses: Modern-Era Retrospective analysis for Research and Applications (MERRA; Rienecker et al., 2011) and MERRA-2 (Gelaro et al., 2017). MERRA was replaced with MERRA-2 in 2016. Both MERRA and MERRA-2 are atmosphere- only reanalyses with MERRA-2 making steady progress towards an IESA by radiatively coupling with the aerosols (Randles et al., 2017; Buchard et al., 2017). The GMAO plans to enhance the level of coupling incrementally within the different earth system components, e.g., ocean-sea ice, constituents/chemistry/carbon, land-hydrology, etc., with data assimilation for each of these components, and for various applications: weather prediction, sub-seasonal to seasonal prediction, and reanalysis. The next major reanalysis from the GMAO, i.e., MERRA-3, will include ocean and sea-ice components coupled to the atmosphere and is a significant leap forward from MERRA-2. This requires integration of an atmosphere-ocean-sea-ice coupled model (AOGCM) with coupled data assimilation.

Currently, the GMAO sub-seasonal to seasonal system (GEOS-S2S; Molod et al., 2020) is the only system that uses the AOGCM with an active ocean. The ocean model is the NOAA-GFDL Modular Ocean Model version 5 (MOM5; Griffies et al., 2012) and initialization is based on a Local Ensemble Transform Kalman Filter (LETKF), as described in Hackert et al. (2020). However, the atmospheric state is from MERRA-2. In order to make progress under the IESA paradigm, the GMAO’s AGCM has been coupled to the state-of-the-art MOM6 (Adcroft et al., 2019) ocean model from NOAA-GFDL. For coupled data assimilation, the GMAO has adopted the Joint Effort for Data assimilation Integration (JEDI), a multi-agency collaboration led by the Joint Center for Satellite Data Assimilation (JCSDA). For this newly implemented AOGCM to be IESA ready at high oceanic resolutions (e.g., finer than 1/4º), the ocean component has to be thoroughly validated and incorporate appropriate subgridscale parameterizations. The ocean model MOM6 is still under development and has not yet been fully validated at high horizontal resolution. Our proposed development and testing strategy would help GMAO accomplish the science goals of an IESA seamlessly with a thoroughly validated ocean (and coupled) model at different resolutions for weather prediction, sub-seasonal to seasonal prediction, and reanalysis. This would enable the GMAO systems to take full advantage of planned and future high-resolution NASA satellite missions (e.g., NISAR, SWOT).

Midlatitude extreme weather events such as cold air outbreaks, heat waves, floods and droughts are responsible for disproportionately large climate-related damage. In recent decades Northern Hemisphere midlatitude continents experienced several unusually cold winters, concurrent with rapid Arctic warming and sea ice loss. The rapid transition from prolonged drought in California in 2012-2016 to severe flooding in 2017 was associated with a switch from atmospheric ridge to trough in the northeastern Pacific. Summer heat waves and related extremes in recent years were associated with quasi-resonance amplification of planetary Rossby waves in the atmosphere. However, it remains unclear how and to what extent the observed changes in midlatitude circulation and extremes were caused by anthropogenic climate change or internal variability in the climate system.

We propose to develop a comprehensive understanding of atmospheric processes responsible for changes in midlatitude circulation and weather extremes (e.g., blocking and atmospheric rivers) in a changing climate, including the internal atmospheric variability, the response to tropical and Arctic forcings, and the role of the stratosphere in the unforced and forced variability. We plan to first extend our previous work on local Rossby wave activity to a unified set of dynamics-based metrics for midlatitude weather systems and moisture transport, based on reanalysis products, retrievals from the Atmospheric Infrared Sounder (AIRS) on NASA’s Aqua satellite, and Global Precipitation Measurement (GPM). Then, we will use these metrics to evaluate midlatitude subseasonal and interannual atmospheric variability in several ensembles of climate model simulations. In particular, we will analyze GISS Model E2.1/E2.2 simulations on the influence of the stratosphere on surface extreme events. Finally, we will examine the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations of midlatitude extremes in the historical period, in the near future, and at the end of the 21st century.

The proposed research directly addresses the theme of “Extremes in the Earth System” in the Modeling Analysis and Prediction (NNH20ZDA001N-MAP). In particular, we will develop robust observation-based measures to evaluate the degree to which midlatitude weather systems and moisture transport, and their impacts on temperature and precipitation extremes are properly represented in Earth system models; we strive to understand the role of internal variability, tropic-midlatitude-Arctic interactions, and stratosphere-troposphere coupling in the observed changes in extreme events. The outcome of this study will help improve the model representation of midlatitude circulation and extremes and thus produce more credible projections for the future. By collaborating with GISS and JPL scientists, this will contribute to NASA’s modeling efforts through the use of satellite retrievals of water vapor and precipitation as observational constraints of midlatitude circulation and moisture transport in climate models.

This proposed modeling effort is a joint Goddard Global Modeling and Assimilation Office (GMAO) and Mesoscale Atmospheric Modeling Group project. The aim is to improve our understanding of cloud and precipitation processes over many scales of motion, from the cloud microphysical scale to the large-scale circulations and improve the moist and turbulence parameterizations in the Goddard Earth Observing System-version 5 (GEOS-5).

Three NASA modeling systems [the cloud-scale Goddard Cumulus Ensemble (GCE) model, the Goddard Multi-scale Modeling Framework (GMMF) and GEOS-5] and their components will be used for the proposed research. Model simulations will be evaluated in a statistically robust manner against ground-based observations, NASA high-resolution satellite data, and reanalysis. Additionally, simulations from the GEOS-5 single column model (SCM) will be evaluated against the GCE and GMMF to improve its parameterization schemes. The major objectives of this proposal are to:
(1) couple the GMMF to the latest cubed-sphere version of GEOS-5,
(2) evaluate the performance of cloud-precipitation processes in microphysics schemes (Goddard 4ICE, RAMS, Morrison, and GEOS-5 Morrison-Gettelman-Barahona) in the Goddard models using NASA observations.
(3) evaluate the effects of dimensionality and convective scale momentum transport on global atmospheric simulations using the GMMF with embedded two- and three-dimensional GCEs,
(4) advance our understanding of cloud-scale responses and their feedbacks in the GMMF system using various large-scale forcing derived from the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis and the “replay” technique in the GEOS data assimilation system (DAS),
(5) use the GEOS-5 single column model (SCM) and sub-grid cloud and turbulence statistics from high-resolution (~200 m) GCE and GMMF simulations to improve the GEOS-5 moist physics and turbulence parameterizations across a wide range of model resolutions.

Relevance: Our proposed research meets the requirements and addresses the research themes as stated in the Modeling, Assimilation and Prediction (MAP) NRA under the category “Cloud in Earth System Models”. It will address the following areas: 1) Cloud processes and their representation in Earth system models (ESMs), 2) Proper representation of precipitation processes, 3) Role of clouds in driving atmospheric circulation patterns from local to global scales, 4) Improving the representation of low clouds in ESMs, and 5) Implementable improvements to current model representations of clouds and cloud-related processes.

Our proposed research will also address programmatic priorities stated in the MAP20 NRA:
(1) Characterize and/or reduce uncertainties in models and products (by evaluating the performance of the GCE, GMMF, and GEOS-5 against NASA satellite and ground observations),
(2) Extend the range of model or product validity by using new components (i.e., improved moist and turbulence schemes in GEOS-5 and GMMF),
(3) Align with the goals and objectives of the core MAP elements (i.e., GEOS-5), and
(4) Enable independent community validation and characterization of the core MAP elements leading to improvement of the model and products (by using national and international field campaign data and ERA5 reanalysis to evaluate the models).As a result of this proposed research, more realistic NASA model simulations of cloud and deep convective processes will be achieved. In addition, this proposed research would provide for the distribution of simulated high-resolution cloud and precipitation data sets through a cloud library.

The launch of a suite of geostationary satellites from the U.S. (TEMPO), Asia (GEMS), and Europe (Sentinel-4) over the current decade marks a new era of satellite observations for air quality studies. They form the “GEO-AQ virtual constellation” to provide unprecedented high temporal observations of atmospheric composition for characterizing the distributions of tropospheric pollutants over the most populated regions in the northern hemisphere. Together with the existing complementary GEO and low earth orbiting (LEO) satellites, the GEO-LEO observing system will not only fundamentally improve air quality monitoring but also facilitate critical quantifications of the most important processes of emission, pollutant evolution, and episodic events that exhibit substantial diurnal variabilities.

We propose a 4-year MAP project using the regional-global modeling system. comprised of the NASA NU-WRF regional model and the GEOS global model, to support the first generation GEO-AQ observations and utilize the observations for air quality research and applications. We have two overarching goals: (1) to perform integrated modeling and analysis of the GEO-LEO data to investigate the key PBL processes determining the air quality in Asia, North America, and Europe, and (2) to develop robust, physically-based methods to convert the GEO-LEO observed (AOD) into surface PM2.5 over a wide range of spatial and temporal scales for air quality applications. In particular, we will address the following science questions:
1. What are the most important interactive/feedback processes involving aerosols that modify PBL meteorology and impair air quality?
2. What are the key factors modulating the AOD and PM2.5 relationships over the scales ranging from local to continental and from diurnal to seasonal?
3. What are the most scientifically robust and logistically feasible ways to convert satellite observed columnar AOD into surface PM2.5 for air quality applications?

We will take the following steps. First, we will enhance the NU-WRF aerosol simulations by incorporating the nitrate and secondary organic aerosol packages used in GEOS into the NU-WRF. Second, we will perform a series of NU-WRF and GEOS model experiments targeting emission sources and physical/chemical processes. Third, we will develop a physically-based machine learning (ML) model that combines observations with the NU-WRF and GEOS derived scientific insights to convert AOD data into PM2.5 in a variety of meteorological and aerosol regimes. Finally, we will use the GEO-LEO satellite data, sub-orbital measurements, ML model and the NU-WRF/ GEOS model experiments to address the science questions laid out above.

The outcome will advance the air quality science in the area of PBL processes, make science-based assessment of the relative importance of factors regulating the AOD- PM2.5 relationship in a wide range of spatial and temporal scales, and develop a physically-based ML model to ingests the GEO-LEO satellite data for air quality monitoring and management over any region.

The proposal responds directly to the MAP call that “modeling is guided by available and anticipated observations with a goal to extract from the observations as much value as possible”, and that “the modeling integrates across all the research activities in NASA’s Earth science research program, and both spans and connects the spatial and temporal scales that characterize satellite observations and observations from ground- and air-based campaigns”. Our project falls under the MAP research themes of “Constituents in the Climate System” and “Coupling in the Earth System” and is particularly relevant to the 2017 Decadal Survey recommended areas of “aerosols and their effect on climate and air quality” and “better understanding and representation of coupling in the boundary layer”.

Urbanization is a dominant global trend. There is growing evidence that urban areas profoundly influence the atmosphere and specifically alter the mean and extreme characteristics of precipitation, resulting in impacts on human welfare, property, economy and ecosystems. Thus, there is an urgent need for research to improve understanding of fundamental mechanisms by which hydroclimate patterns are altered. Quantifying the impact of urbanization on clouds and precipitation has been to date extremely challenging because: (1) changes in precipitation patterns may occur at substantial distances from the city; (2) precipitation events are highly variable and require appropriate statistical methodology to quantify their uncertainty; (3) detailed and coherent observational records of three dimensional cloud/precipitation properties are limited; and (4) numerical model parameterizations only partially capture the scales of variability and features of precipitation/hail events.

This investigation leverages new observational and modeling capabilities to quantify the impact of urban areas on precipitation and hail, as well as on the occurrence of hailstorms and extreme precipitation at the local/regional scale for multiple urbanization scenarios. It will thus contribute to NASA’s Modeling, Analysis and Prediction (MAP) program theme of Extremes in the Earth System and will address the following research objectives:
1) Identify the key physical and chemical drivers that dictate changes in frequency and intensity of extreme precipitation and hailstorms, particularly with respect to the role of land use change and aerosols associated with urbanization.
2) Identify drivers of the regional variability in the urban feedback on extreme precipitation and hail, and the role of the background climate.
3) Quantify the spatial and temporal scales of impact of urbanization on precipitation both in urban and downwind regions.

Multiple urban areas, characterized by different background climate, urbanization level and size, will be selected to investigate feedback mechanisms between urban/climate forcings and precipitation in contemporary conditions. Our integrated interdisciplinary research approach leverages three key components: (i) remote sensing observations, (ii) numerical model simulations, and (iii) advanced statistical models. Our research approach uses observational remote sensing data (from ground based dual polarization RADAR and satellite born RADAR and radiometers) to identify key characteristics in the intensity, frequency and spatial patterns of current precipitation events that will be further investigated through numerical model simulations. High resolution simulations with the NASA Unified Weather Research and Forecasting (NU-WRF) model will be designed to elucidate fundamental mechanisms and physical/chemical drivers of changes in precipitation patterns and occurrence of extremes by developing an ensemble of simulations where perturbations to the control runs are applied to explore the impact of different urbanization/land use and emission scenarios. A Bayesian statistical framework, accounting for the non-Gaussian properties of precipitation data, will be developed to identify the most significant drivers in the simulated changes of precipitation characteristics and associated spatial patterns.

The proposed research is aligned with the MAP scope as it offers a fundamental advance to the understanding of the Earth system by investigating impacts of anthropogenic forcings on the local/regional climate. It also relies on the use of remote sensing and satellite data, specifically latest advances in RADAR technology and high resolution satellite observations of cloud properties which are combined with numerical model simulations that allow a deeper investigation of the physical/chemical processes and forcings responsible for the observed changes in precipitation.

Extreme precipitation events that occur on multi-week time scales (including rapid-onset droughts and major river flooding events) are an undeniable source of major societal and economic disruption. At the same time, these events are challenging to predict beyond the weather prediction time frame of 7-10 days in the future, as demonstrated by modern state-of-the-art forecast models. The purpose of the proposed research is to enhance our physical understanding of the mechanisms underlying such extreme precipitation events in direct support of improved prediction capabilities of these events on subseasonal (2 weeks to 2 months in the future) time scales. Doing so would prove invaluable for enhancing our preparedness for future extreme events.

We will comprehensively study warm-season (spring through fall) wet and dry precipitation extremes that have impacted large regions of North America during the satellite era (1981 onward) and have occurred on multi-week (or longer) time scales. A key objective is to elucidate the physical processes leading to each event’s onset, maintenance, and demise. Another key objective is to study the prediction skill of the events in current forecast systems, and ultimately guide model development toward improving the prediction of the physical processes that caused the events. The specific research plan is as follows: First, we will identify extreme precipitation events and perform a preliminary investigation of their associated physical mechanisms using NASA’s state-of-the-art MERRA-2 reanalysis and supplemental datasets. Second, we will identify the underlying physical causes (i.e., drivers of the relevant teleconnections and atmospheric Rossby wave trains) of the events by applying MERRA-2 to the GEOS prediction system, a model developed by NASA’s Global Modeling and Assimilation Office (GMAO). Finally, we will investigate the subseasonal prediction of the events, with the ultimate goals of revealing model biases and other errors that limit prediction skill, informing model development at GMAO, and developing in-line empirical corrections for reducing those biases and consequently improving subseasonal forecasts. A key and unique aspect of the second phase of the proposed work is employing a “replay” capability developed in the GMAO that allows one to constrain the model atmosphere to remain close to an analysis, greatly facilitating the isolation and detection of the underlying mechanisms for extreme events (an approach that has proved effective in a recent study of the flash drought in the southeastern United States in 2019).

In summary, the proposed research will take advantage of unique capabilities afforded by NASA’s GMAO, including state-of-the art modeling, high-quality reanalysis, and newly demonstrated techniques (i.e., regional replay) for understanding physical processes within the climate system. Through a better understanding of the mechanisms linking precipitation extremes with their remote and local sources, and through efforts to improve the representation of those mechanisms in models, the proposed work is expected to result in scientific advances relevant to the “predictability in the Earth System” and “extremes in the Earth System” research themes of the Modeling, Analysis, and Prediction program. Moreover, the proposed work is also broadly relevant to NASA’s Earth Science Research Program goals of “improving the capability to predict extreme weather events” and “better understanding the roles and interactions of the oceans, atmosphere, land, and ice in the climate system.”

Land states affect the atmosphere through their control on surface fluxes, and the subsequent impact of fluxes on planetary boundary layer (PBL) properties. Thus, land states are potential sources of predictability. Whether looking at subseasonal prediction or responses to long-term climate change, numerical models need to simulate the coupled land-atmosphere (L-A) processes well in order to represent the atmospheric response to anomalies in land surface states. This includes links from land surface states through surface fluxes, near surface meteorology, PBL characteristics and ultimately cloud formation and precipitation, tracing through both the energy and water cycles.

Metrics of L-A coupling come in two main categories: physically-based metrics derived directly from process understanding, and statistically-based metrics that may be informed by physics but do not explicitly represent processes. In the proposed work, we will draw on both. We will use information theoretical approaches to provide non-parametric, non-linear, assumption-free analyses, unlike traditional linear statistics that L-A metrics have traditionally used. We will also use the process-based Heated Condensation Framework (HCF) to describe land-surface PBL interactions that connect surface heat flux partitioning to PBL development and the potential for cloud formation. High-quality observationally based analyses of land states and surface fluxes with global coverage are ideal for diagnosing L-A coupling and are amenable to the non-parametric statistics.

Three questions will be posed and addressed:1. Can we construct, from observational (mainly satellite-derived) and reanalysis data, a global depiction of the spatiotemporal patterns of L-A coupling as a multivariate network of processes through the energy and water cycles?2. How can the statistical and physical depictions of this coupling network be combined to reveal the places and times where changes in land surface states affect PBL evolution, and thus the quality of forecasts and projections?3. How well do current global Earth system models replicate observed coupled L-A behavior?

Process Networks (PNs) are an application of transfer entropy that can quantify links and chains among a web of variables such as the known process linkages in the water and energy cycles linking land and atmosphere. PNs have never been applied in a distributed fashion across continental to global domains before. Comparing PNs among observations and model configurations (reanalysis or “open loop”) can illuminate fundamental model problems that contribute to persistent forecast errors. Lastly, PNs of reanalysis vs observations would show how model physics represents such multivariate linkages. HCF will provide a physically-based elaboration to the PN results.

The proposed project will utilize global satellite-based data for precipitation, soil moisture and surface fluxes. These will be complemented by reanalysis data (MERRA-2 and ERA5) which are observationally constrained but not free from model bias, especially with regard to fluxes. Global observed PBL data (maximum daily height, cloud base) are not available. Reanalyses provide global coverage, CALYPSO and AMDAR can provide local samples with broad coverage.

We will employ additional simulations with the GEOS model, particularly installing on-board diagnostics of key coupling metrics – code is available from an existing GEWEX project ( and has been implemented successfully in other Earth system models. This can be expanded to include information theory based adaptations of key L-A metrics.

The proposed project will deliver global data sets of PN results depicting L-A process linkages based on observations and reanalyses, diagnosis of GEOS model performance, deviations from observationally-based metrics, plus peer-reviewed publications and conference presentations.

Land-atmosphere interactions constitute the largest source of climate predictability at 7-30 day timescales. Conventionally recognized as merely a lower atmospheric boundary condition, the land model is today viewed as an integral Earth system model component. Vegetation, soil moisture, and groundwater states vary on timescales of weeks to years, and, through land-atmosphere interactions, significantly modulate regional climate, including the severity and duration of drought.

A major concern for Earth system model development is that complex, coupled processes like land-atmosphere interactions are not sufficiently constrained by observations, and that efforts to add further process complexity to Earth system models are potentially only introducing compensating errors. Although NASA global satellite-based evapotranspiration, precipitation, terrestrial water storage change, soil moisture, and snow products are widely applied in univariate analyses, they are only occasionally applied in an integrated fashion in the context of constraining Earth system model development.

The proposed 4-year research project will integrate multiple NASA satellite remote sensing datasets, including ECOSTRESS, SMAP, and GRACE, into a hierarchical NASA Unified Weather Research and Forecasting (NU-WRF) modeling and data assimilation study of land-atmosphere processes and feedbacks during drought dry-down and recovery. A distinguishing aspect of the proposed work is a focus on the development and application of a novel configuration of the NOAA National Water Model that includes parameterization of dynamic vegetation root–groundwater interactions. This parameterization will enable partitioning of groundwater loss to shallow and deep-rooted vegetation across the landscape, which has not before been possible.

The proposed 4-year research project will integrate multiple NASA satellite remote sensing datasets, including ECOSTRESS, SMAP, and GRACE, into a hierarchical NASA Unified Weather Research and Forecasting (NU-WRF) modeling and data assimilation study of land-atmosphere processes and feedbacks during drought dry-down and recovery. A distinguishing aspect of the proposed work is a focus on the development and application of a novel configuration of the NOAA National Water Model that includes parameterization of dynamic vegetation root–groundwater interactions. This parameterization will enable partitioning of groundwater loss to shallow and deep-rooted vegetation across the landscape, which has not before been possible.The proposed research will address the following three fundamental science questions: 1) To what extent does the added representation of vegetation–groundwater coupling modulate modeled drought stress response in heterogeneous landscapes across the globe and are the inherent process sensitivities verifiable by in situ and satellite remote sensing observations? 2) Can remotely-sensed fine-scale vegetative drought stress response across a heterogenous landscape experiencing dry-down and recovery be used to constrain model stomatal, rooting depth, and root–groundwater parameterizations? 3) Does improving the groundwater–vegetation–atmospheric processes lead to better partition of groundwater fluxes into atmosphere and streamflow components, consequently improving the representation of landscape-scale land–atmosphere feedbacks?

Following participation in a large international land model intercomparison effort spanning 170 sites globally, this research will focus on three recent drought cases on three different continents, centered on well-instrumented catchments. Offline land model and single column modeling will be used to identify key coupled process relationships, sensitivities, and uncertainties, and to formulate potential model refinements. Subsequent regional NU-WRF simulations with and without SMAP vegetative optical depth and soil moisture and GRACE terrestrial water storage data assimilation will be employed to test the robustness and transferability of these initial findings, as well as to evaluate aggregate drought stress response on regional climate. The proposed research will identify critical processes in the drought stress response across local, landscape, and regional scales and characterize both their predictability and their verifiability from in-situ and NASA satellite assets. The results will steer future Earth system model development and inform future prioritization of NASA asset development. Novel insights are expected in terms of vegetation–groundwater and land–planetary boundary layer structure relationships, streamflow, groundwater recession, and regional precipitation recycling during drought.

Background:One of long-term goals of general circulation models (GCMs) is to provide long-term prediction of high-impact convective phenomena such as tropical cyclones, flooding, and monsoon. For predicting them, it is crucial to adequately represent the tropical wave features including the convectively coupled equatorial waves (CCEWs) and Madden-Julian Oscillation (MJO). However, simulating those tropical waves in GCMs has remained challenging, causing uncertainties in the fidelity with which GCMs can predict future changes in high-impact convective phenomena.

Objectives:The Science PI’s recent work revealed that the scale-dependencies of two convective properties are crucial for the mechanisms of the CCEWs and MJO: 1) scale-dependencies of vertical wind structures and 2) of cloud-radiation feedbacks. Theoretical literature suggests that these two properties must be adequately represented in GCMs for better simulating the CCEWs and MJO. Our research will, therefore, focus on those scale-dependencies, and achieve the following goals: 1. to assess the scale-dependencies of vertical wind structures and cloud-radiation feedbacks in four different configurations of ModelE3, and to evaluate them by comparing with MERRA2, ERA5, and satellite observations;2. to conduct a series of parameter changes based on the default runs of the four ModelE3 configurations; and 3. to identify the sensitivities of those scale-dependencies to the model changes, and evaluate the robustness of the sensitivities.

Approach:The scale-dependencies of vertical wind (omega) structures and cloud-radiation feedbacks are examined with novel diagnostic techniques introduced by the Science PI’s recent work. For quantitatively assessing omega profiles on various scales, two quantities are defined: 1) the top-heaviness ratio, and 2) the tilt ratio. The top-heaviness ratio represents how top-heavy an omega profile is at the peak of a wave; and the tilt ratio represents how much tilt omega profiles contain in the spatio-temporal evolution of a wave. These two quantities will be computed with MERRA2, ERA5, and multiple satellite observations. Additionally, we will also compute the cloud-radiation feedbacks on various scales. Then, the top-heaviness ratio, tilt ratio, and cloud-radiation feedbacks are used for evaluating and constraining the crucial properties in the ModelE3 for improving model’s skill in simulating the CCEWs and MJO. The NASA GISS has multiple configurations of ModelE3, whose tuning processes were optimized with a novel machine learning algorithm. Additionally, we also have one configuration with an alternate entrainment scheme. As a result, we have four different ModelE3 configurations whose parameters vary substantially, but climatology is all realistic. These different configurations provide us with a unique opportunity to identify the effects of changed parameters in the same climatology, and to evaluate the robustness of the sensitivities of target properties to the model changes. We will conduct a series of sensitivity experiments by changing parameters such as the “precipitation efficiency”, “hydrometer terminal velocity”, “re-evaporation rate of precipitation”, and “entrainment rate”. These sensitivity experiments will be conducted based on the default runs of the four ModelE3 configurations mentioned above to evaluate the robustness of the sensitivities.

Relevance to solicitationOur proposed research addresses the MAP research theme of “Clouds in Earth System Models”. Our project will help improve the GISS-GCM’s skill in simulating the CCEWs and MJO by constraining the key properties, namely vertical wind structures and cloud-radiation feedbacks, with observations, the newly developed diagnostic techniques, and the unique sensitivity experiments based on the four different ModelE3 configurations. This approach addresses the programmatic priority to “help reduce uncertainties in the models”.

Objectives: Earth System Models (ESMs) suffer from biases in cloud properties. These biases can be partially mitigated by tuning free parameters associated with microphysics or other cloud- related parameterizations. Different modeling centers apply different tuning approaches to find optimal parameter values, from heuristic approaches to more systematic methods using Perturbed Parameter Ensembles (PPEs). A common tuning framework across multiple models could better leverage “bottom-up” process-level parameterization advances and global observational datasets for model constraint. A consistent “top down” framework for con- straining model parameters could also help quantify uncertainty at the process level across models, which for many cloud-related parameterizations is likely large but remains mostly unquantified. In particular, for cloud microphysics uncertainties exist at all levels of scheme complexity and scale.

Proposed research: We propose to develop compatible PPEs across two NASA models – GISS Model E and GEOS – as well as the Community Earth System Model (CESM). We will also implement an artificial neural network-based emulator to map uncertain parameters to the climate state for comparison with observations. We will then use NASA satellite observations as an observational constraint to optimize parameters. Our work has several novel aspects. We will use models with a common cloud microphysics parameterization; all three models use the Gettelman and Morrison (2015) microphysics scheme (MG2). We will modify MG2 to incorporate a Bayesian framework (using the Bayesian Observationally-constrained Statistical-physical Scheme – BOSS) to represent warm cloud processes in all three models. BOSS enables rigorous application of observational constraints to microphysical process rates, and is well suited for a PPE framework. We will use box and one-dimensional kinematic models for “bottom- up” constraint of BOSS using detailed process representations from existing bin and bulk microphysics models. We will explore working with PPEs in both single-column models and global models. A focus will be on the representation of microphysics in shallow warm clouds which have a critical impact on climate sensitivity. Our efforts will integrate both “bottom-up” and “top-down” constraints on microphysics parameterization uncertainty.

Expected outcomes: The main objective of the proposed research is to improve process-level and global constraint of warm microphysics in GISS Model E, GEOS, and CESM. Best-estimates of micro- physical parameterization uncertainty will be informed by both bottom-up (process-level) constraints and top-down (global satellite observational) constraints. An emulator-based PPE approach will also facilitate constraint and tuning of these models when new parameterization developments are tested in the future (for all parameterizations, not just microphysics). Model data, observational data for model constraint (including uncertainty estimates), and the code for the PPE-based framework will be made available to the wider community. A multi-model PPE framework will also provide a better community assessment of structural uncertainty in our ability to represent the climate system and its future trajectory. This proposal is highly relevant to the NASA Modeling, Analysis and Prediction (MAP) program solicitation. The work here is central to the “Clouds in the Earth System” theme, and on clouds in Earth System Models, including process representations of low stratiform clouds that strongly impact climate sensitivity. We will use NASA observations and also provide uncertainty information for a path to improve current models.

Upwelling and mixing in the equatorial Pacific are key manifestations of large-scale ocean circulation associated with ENSO. In coordination with the activities of the OceanPredict Observing System Evaluation Task Team (OSEval TT), we will assess the differential impact on ENSO predictability of all routine observing systems including satellite and in situ observations of sea level, temperature, and salinity. Our particular interest will be to assess how these observations (within the constellation of other satellite and in situ observations) will affect the mixed layer, buoyancy forcing, and SST in the Pacific region with implications for air-sea coupling and ENSO prediction.

We will address the following key science questions: What observation types and where should these observations be taken to optimize their spatial and temporal impact on coupled predictions? What technologies (e.g., mooring, floats, profilers, satellites) would be most appropriate to observe key phenomena of interest associated with intra-seasonal predictability? What would be the best utilization of new observational technology (such as gliders, unmanned vehicles, saildrones, etc.)? How do we obtain/adjust the error estimates assigned to each observation type (within the context of data assimilation), such that the state estimate is optimized as the observational network changes? Our overarching approach is to execute a hierarchy of observing system experiments (OSE) with multiple NASA-supported assimilation systems that isolate different observation data sets, one by one, to assess the impact of each type. By addressing these key science questions, our goal is to improve model parameterizations and data assimilation and provide a better understanding of the tropical Pacific air-sea interaction. An understanding of observation impact will also support the design of an optimized observing system targeted at improved seasonal global predictions.

To address all these science questions, we plan to utilize the seasonal coupled forecast production model for NASA GMAO, the Sub-seasonal to Seasonal (S2S) forecast system V3. Forecasts from this coupled model are routinely submitted as the NASA contribution to the North American Multi-Model Ensemble (NMME) project. This version couples the 0.5° resolution, 72 levels GEOS-5 atmospheric model with the Modular Ocean Model Version 5 [Griffies, 2012] with 0.25° resolution and 50 vertical levels. For the current configuration all available along-track absolute dynamic topography [AVISO, 2013], in situ observations (TAO/RAMA [McPhaden et al., 2010], Argo [Roemmich et al., 2009], XBT, CDT, etc.) and Aquarius [Lilly and Lagerloef, 2008], SMAP [Fore et al., 2016] and SMOS [Boutin et al., 2018] satellite salinity data are assimilated using a scheme similar to the LETKF of [Penny et al., 2013]. To address model and assimilation system dependency, all OSE experiments will be repeated using the Estimating the Circulation and Climate of the Ocean (ECCO) Version 4 [Forget et al., 2015] system. ECCO uses the MITgcm ([Marshall et al., 1997]) with nominally 1° global resolution (equatorially telescoped to 1/3°) with an adjoint-based long-window assimilation system.

This work is designed to address some of the main research themes of CLIVAR: 1) upwelling dynamics, 2) ENSO variability and predictability, and 3) circulation, climate variability and change and tackles all of the goals of OceanPredict OSEval-TT. This work also directly addresses several of the research themes of the MAP solicitation, namely Coupling in the Earth System (via improved understanding of ocean mixed layer and consequences for air/sea interaction) and Assimilation (assessment of oceanic observational error). Finally, this proposal primarily focuses on Predictability in the Earth System research theme of MAP since we plan to assess the impact on coupled predictions of changing the Tropical Pacific observing systems.

The 1991 Mount Pinatubo eruption resulted in a significant, anomalous reduction in the annual growth rate of net CO2 surface fluxes to the atmosphere. No Earth System Model (ESM) has yet reproduced that CO2 record. The Pinatubo eruption ejected large amounts of sulfur dioxide that altered the quantity of aerosols around the globe, dramatically cooled the climate, and impacted the activity of the biosphere, resulting in the CO2 anomaly. All of these effects were interactive and highlight that the carbon cycle involves numerous couplings.

A suite of NASA satellite observations can be used to constrain the physics of ESMs, for which the Pinatubo eruption would then serve as a key hindcasting benchmark for testing the ability of ESMs to predict the future trajectory of the carbon cycle and climate change. Thus far, ESM simulations have produced an insufficient carbon sink on land (diffuse radiation enhancement of photosynthesis versus cooling of soil and plant respiration) to account for the post-Pinatubo anomaly, but aspects of the carbon cycle not yet fully investigated include potential suppression of fire from cooler temperatures and the role of fire and volcanic ash nutrient fertilization of ocean biogeochemistry. Multiple contributions to the Pinatubo effect on atmospheric CO2 can only be quantified through interactive models.

We propose to quantify these multiple effects through a novel coupling of carbon and atmospheric chemistry, with land and ocean biogeochemistry, in the NASA Goddard Institute for Space Studies Earth System Model (NASA GISS ESM) ModelE. ModelE’s several component modules can treat most of the known perturbations by the Pinatubo eruption separately, but are currently only partially coupled. Currently in the GISS ESM the carbon cycle and the atmospheric chemistry are largely independent of each other, asthe first simulates CO2 while the latter simulates reactive species that affect aerosols.

We propose to couple through fire and dust dynamics the GISS ESM’s carbon submodels -- the Ent Terrestrial Biosphere Model (Ent TBM) and the GISS version of the NASA Ocean Biogeochemistry Model (NOBM) – and the chemistry physics submodels -- atmospheric chemistry and the pyrE fire model. The primary model development proposed is to couple vegetation-fire disturbance with the pyrE module, add peatland fire and crops biomass burning, and to apply tracers to fire emissions and ash from Pinatubo for deposition as ocean nutrients. The fire model will be trained on satellite-observed fire and leaf area index (LAI), and on CO2 observations for hindcasting Pinatubo period. Pinatubo’s eruption will be simulated with a volcanic emissions model already in ModelE, and the contributions of different Earth components quantified in tiered experiments.

This proposal will impact the field by coupling the responses of multiple aspects of the carbon cycle and atmospheric chemistry to the 1991 Mount Pinatubo eruption quantify their contributions to the observed anomaly in the CO2 growth rate. It will demonstrate the signficance of coupling both carbon and chemistry dynamics for predicting the effects of perturbations to the Earth’s climate and carbon cycle.

This proposal is relevant to NASA’s Earth Science key question of “How will the Earth system change in the future?”, as it investigates processes sensitive to climate change. This proposal supports the NASA GISS Global Modeling project as a core activity of the Modeling, Analysis, and Prediction (MAP) program, and is relevant to the research themes of “Coupling in the Earth System” and “Predictability in the Earth System”, as it introduces more coupled, prognostic capability to the NASA GISS ESM.

Droughts are becoming hotter, more frequent, and more extreme. This increasing drought stress reduces the land surface’s capacity to take up carbon through photosynthesis and reduces available water resources. By modulating surface turbulent fluxes, droughts can also affect their own propagation and change atmospheric patterns more generally. However, despite decades of intense research, most land surface models – including Catchment-CN, the land surface component of the GMAO GEOS-5 Earth System Model - still cannot capture the magnitude and temporal variations in plant responses to drought. This limitation arises from structural and parameter uncertainties in the modelling of stomatal conductance.

The response of stomatal conductance to drought stress is usually modelled with empirical functions of soil moisture and atmospheric humidity. However, this ignores the physiological regulation of stomatal conductance by leaf water potential, which is affected by the flow of water from roots to leaves, i.e. plant hydraulics. Incorporating plant hydraulics is known to improve modelled of plant drought response, but it is computationally expensive and difficult to parametrize. We propose to build a simplified plant hydraulics-like emulation scheme that reparametrizes the Catchment-CN empiricalstomatal conductance formulation to mimic the response to soil moisture and atmospheric humidity of a full plant hydraulic model, without the need to explicitly simulate leaf water potential. This approach is significantly less computationally intensive and more tractable.

The second thrust of this proposal is to build an alternative accounting for spatial variations in vegetation drought response. Catchment-CN – like other models – parametrizes stomatal traits based on plant functional types (PFTs). However, plant hydraulic traits are highly variable within PFTs, depending on species cover, stand age, etc. Alternative parametrization methods are needed that account for this diversity in behavior. We will use an indirect approach to derive ‘hydraulic functional types’ – predictions of spatial patterns of variation in stomatal conductance depending on environmental filtering.

Specifically, we willa) Use a model-data fusion approach to retrieve the most likely plant hydraulic parameters across the globe, constrained by observations of canopy water content from vegetation optical depth and ALEXI evapotranspiration estimates.b) Because these maps are imperfect, we will use them to derive optimal, data-driven ‘hydraulic functional types’ – equations relating stomatal trait spatial patterns to variations in average climate, canopy height, and other landscape factors. c) Next, we will incorporate the proposed approximation of plant hydraulic traits in the Catchment-CN model, calibrating the magnitude of each trait to the model (to avoid compensating errors), but not its spatial patterns.d) We will evaluate the new Catchment-CN stomatal conductance parametrization by comparison with control runs in terms of both land surface states, fluxes, and through examination of the response to specific drought response. We will also run coupled simulations using the full GEOS to test the effect of improved of plant drought response on land-atmosphere coupling.e) Lastly, we will use the new scheme and model-data fusion approach to emulate CLM5 stomatal traits, and study how these behave. This will provide insight into how CLM5 simulates different aspects of plant drought response, which will help planned efforts to merge CLM5 with Catchment-CN.

Our proposal addresses both the ‘Extremes in the Earth System’ and ‘Coupling in the Earth Systems’ components of the call. By carefully considering the role of spatial variations in vegetation parameters, rather than the parameters themselves, we will create a new approach to parametrization that can be used for simulating a variety of other plant processes.

Atmospheric Rivers (AR) - elongated filaments (>2000 km) of abundant concentration of water vapor in the low and mid-troposphere, are the major sources of water supply, and causes of extreme precipitation events (EPE) causing disastrous flash floods and mudslides in western North America (WNA). Numerous studies have shown that AR/EPEs affecting coastal regions of WNA during boreal winter stem from multi-scale interactions of atmospheric processes involving transitions of the Madden and Julian Oscillation (MJO) states, and associated tropical convection and moisture plumes, variability of the subtropical jetstream and downstream wave amplification. However, skills in numerical prediction beyond the weather scales (~ 5days) of AR/EPEs remain low. Because of its relative slow time scale (~30-60 days), the MJO state-transition is considered a key source of S2S predictability of ARs/EPEs over WNA. Yet, simulations of MJO propagation are still poor in state-of-the-art climate models. One of the key problems is the difficulty in simulating realistic diurnal variability of convection over the Maritime Continent (MC) and associated cloud-radiation feedback processes, which are known to interact strongly with propagation of MJO convection from the MC to the equatorial western Pacific. The proposed research will be focused on AR/EPEs affecting the WNA, with the objectives to a) better understand the multi-scale processes involving radiation-cloud-convection-circulation interaction (R3CI) that underlie the S2S potential predictability, and b) improve S2S predictions through assimilation of satellite all-sky radiance data over the tropics. Four research tasks are proposed.

Task 1: Observational analysis: Examine causal factors contributing to S2S predictability of AR/EPEs affecting WNA, focusing on MJO propagation from the Indian Ocean, through the MC to the equatorial western Pacific, and associated changes in extratropical circulation. Particular attention will be devoted RC3I processes linking cloud radiative perturbations and induced dynamical adjustments of the jetstream to Rossby wave breaking over the Northeast Pacific. Data from MERRA2 reanalysis, Cloudsat, MODIS cloud regimes, GPM/IMERG precipitation, CERES Radiative fluxes and Cloud properties and MERRA2 reanalysis will be used.

Task 2: Model Intercomparison: Experiments with the GEOS5 model will be carried out to examine the impact of R3CI on MJO propagation and predictability of AR/EPEs. Model intercomparison analyses will be conducted, together with model outputs from CFMIP (Cloud-Feedback Intercomparison Project) validated against observed diagnostic developed in Task 1.

Task 3: Satellite all-sky radiance data assimilation: Six-hourly satellite radiance data over the MC region will be assimilated into the GEOS Forward Processing System (GFP), by enhancing and extending existing radiance assimilation techniques for all-sky GMI to include AMSU-A, MHS and ATM. Focus will be on the impacts on precipitation diurnal cycle variability over the MC, and associated cloud radiative perturbations impacts on atmospheric teleconnection over the North Pacific validated against GPM/IMERG, MODIS-CR and MERRA2 re-analyses.

Task 4: S2S predictability experiments: GEOS5 ensemble re-forecast experiments with be conducted for select AR/EPEs for different lead times up to 15-30 days with all-sky radiance assimilation a) over the entire tropics, and b) over all tropics but with the assimilation over the MC regions withheld. Differences between the two experiments will be used to assess impacts of assimilating diurnal variability of satellite radiance in improving MJO propagation, and S2S potential predictability of ARs/EPE.

We propose a project focused on tropical cyclones (TCs) and associated precipitation in the NASA Goddard Earth Observing System Subseasonal to Seasonal (GEOS-S2S) prediction system that has been developed at the NASA Global Modeling and Assimilation Office (GMAO). Despite the improvements in global weather prediction systems, subseasonal prediction of TC genesis, track, and intensity, let alone landfall and TC-associated precipitation, has remained challenging. Our project aims (1) to assess predictability and prediction skill of subseasonal TC prediction and (2) to identify the origins of forecast errors. A range of performance- and process-oriented TC diagnostics will be applied to the GEOS-S2S reforecasts. In particular, we will examine the biases in the large-scale environment of TCs, as well as biases in their storm-scale processes. We will use both the current (GEOS-S2S-2) and next (GEOS-S2S-3) versions of GEOS-S2S.

To accomplish our goals, we will first thoroughly document the ability of GEOS-S2S to forecast TC activity at subseasonal timescales. We will calculate probabilistic forecast skill of TC activity at the basin and regional scales. Skill scores targeting TC genesis, track, intensity, and landfall will be compared to those of other state-of-the art forecast systems. The GEOS-S2S's ability to simulate TC precipitation will also be evaluated.

Second, we will investigate whether and to what degree prediction skill varies with the climatological seasonal cycle and modes of large-scale variability that modulate TC activity. We will analyze biases in the model's mean state, and their relationship to the skill scores. We will assess the GEOS-S2S's ability to capture the ENSO-TC and MJO-TC relationships and their impact on skill scores.

Third, we will examine the kinematics and thermodynamics of simulated TCs using a set of process-oriented TC diagnostics. Azimuthally-averaged structures of winds, temperature, moisture, as well as radiative and surface turbulent fluxes will be constructed and compared to the reanalysis and observational datasets, including MERRA-2 and NASA GPM. A particular focus will be on the distribution and intensity of precipitation around TCs. Another focus will be the moist static energy variance budget which can unveil crucial feedback processes controlling TC formation and intensification.

Lastly, we will design and apply the tendency bias correction (TBC) scheme within the framework of GEOS-S2S-3. TBC applies pre-calculated tendency corrections online to reduce the model's systematic large-scale error. The design of our new TBC simulations will be guided by the results of our diagnosis of GEOS-S2S reforecasts and will target improving TC predictions that are relatively poor (e.g., Atlantic TCs with initial MJO phase 7). The performance- and process-oriented diagnostics will be applied to the TBC reforecasts to determine the extent to which improving the large-scale biases enhances the model's TC prediction skill.

The proposed project will improve our understanding of subseasonal TC predictability in the GEOS-S2S system, which is related to MAP's core research activity for GMAO GEOS-S2S development. While we focus on the GEOS-S2S system, our findings will shed light on general subseasonal predictability limits for TCs and their associated precipitation and possible pathways for improved prediction skill.

The stratospheric polar vortex refers to the strong stratospheric circumpolar westerly jet over both the Arctic and Antarctic during the cold-season months. The stratospheric polar vortex has large variability, caused by the breaking of upward propagating Rossby waves from the troposphere. The stratosphere affects surface weather and climate through the downward coupling of stratospheric polar vortex variability to tropospheric circulation variability. There is clear evidence that stratospheric polar vortex variability is an importance source of surface subseasonal-to-seasonal (S2S) predictability. However, current S2S predictions systems are deficient in representing polar vortex variability and its downward coupling because they do not use interactive stratospheric ozone chemistry. By coupling with stratospheric polar vortex variability though ozone-radiation and ozone-dynamics feedbacks, interactive ozone chemistry provides a potential source to improve S2S predictions. We propose to implement interactive stratospheric chemistry scheme into the Goddard Earth Observing System – Subseasonal-to-Seasonal prediction system (GEOS-S2S) and quantify the effects of stratospheric ozone-radiation-dynamics coupling on S2S forecasts and atmospheric and oceanic variability.

We will use the new configuration of GEOS-S2S to perform free-running simulations and retrospective S2S forecasts. Combined with observations, we will address the following science questions: 1) How does interactive ozone affect modeled stratospheric polar vortex climatology, variability, and stratosphere-troposphere coupling? What are the mechanisms? 2) Can interactive ozone enhance S2S forecast skill? 3) How does stratosphere and interactive ozone influence oceanic variability? 4) How does stratospheric surface impact affected by “world avoided” high loads of ozone depleting substances?

We propose to perform five tasks: 1) Implement an interactive stratospheric chemistry into GEOS-S2S. 2) Conduct free-running perpetual 2015 simulations with and without interactive ozone to evaluate the effects of interactive ozone on stratospheric polar vortex climatology, variability and stratosphere-troposphere coupling. 3) Conduct retrospective S2S forecasts with interactive ozone for 1999-2015. Quantify the effects of interactive ozone on subseasonal forecast of Arctic stratospheric sudden warming events and their surface impact. Quantify the effects of interactive ozone on seasonal forecast of SH surface anomalies and climate extremes. 4) Understand the impact of Arctic SSW events on the North Atlantic Ocean variability. Identify the impact of Antarctic polar vortex variability on Southern Ocean phytoplankton. Investigate how interactive ozone influence these effects. 5) Conduct a “world avoided” simulation of high levels of ozone depleting substances and investigate how stratospheric impact on surface is affected in this extreme case.

Our proposed work will improve GEOS-S2S with implementation of interactive stratospheric chemistry mechanism. We will improve the understanding of stratosphere-troposphere coupling as a source of subseasonal-to-seasonal predictability. The proposal’s research will advance the understanding of stratospheric impact on atmospheric and oceanic variability

The proposed research activities fit the solicited MAP research themes of “Predictability in the Earth System”, “Coupling in the Earth System”, and “Extremes in the Earth System”. This proposal addresses “predication and predictability at S2S timescales”. It aims to “better understanding and representation of the coupling among atmosphere, surface waves, ocean, sea ice, land, and biosphere”. Our proposed work helps to ““understand the interconnections in the Earth system which results in the extreme behavior, and to improve the model representation of the key underling processes”.

The tropics, particularly the tropical convective zones, are the heat engine of the global general circulation. This circulation connects to subtropical boundary-layer clouds through the descending branches of the Hadley circulation and the subtropical trade winds which converge in the tropics to form the intertropical convergence zone (ITCZ). Since the boundary-layer clouds play an important role in determining climate sensitivity, the accuracy of the interactions between hydrometeors and radiation over the convective zones and their connection to the coupled atmosphere-ocean system over the tropical and subtropical Pacific and Atlantic is critical.

Our prior works shows that the radiation calculations of most CMIP5 and CMIP6 (Coupled Model Intercomparison Project Phases 5 and 6) global climate models (GCMs) are performed with greatly underestimated total ice water path (TIWP) over the convective zones such as the ITCZ, South Pacific convergence zone (SPCZ) and tropical western Pacific (TWP). We identified significant differences in simulated radiation, circulation, precipitation and surface properties induced by this lack of TIWP. This is at least in part due to excluding or misrepresenting convective- and stratiform falling ice radiative effects (FIREs). The exclusion of FIREs destabilizes convective regions by increasing upper level longwave cooling, leading to low-level anomalous outflow, which ultimately results in weaker trade winds, causing biases in the simulated trade wind boundary layer and upper ocean.

We propose a comprehensive model-data evaluation in terms of cloud-radiation-circulation coupling within the atmosphere and its connection with the upper ocean over the subtropical and tropical Pacific and Atlantic oceans. We will analyze the controlled simulations with the new NASA GISS ModelE3 (GISS-E3) model, which has implemented advanced prognostic falling ice radiative effects, output from the CMIP6 ensemble, and NASA satellite observations. We will compare present-day climate simulations with observational products in terms of climatology, seasonal cycle and internal variability such as the progression of El Nino events, and report on the differences in the simulated present-day and projected climate introduced by the inclusion or exclusion of FIREs.

Our primary research tasks include: (1) compare GISS-E3 and observed tropospheric hydrometeors and their radiative effects over the convective zones and trade wind regions; (2) quantify biases in the cloud-radiation-circulation coupling from local convective zones to the trade wind boundary layer; (3) quantify biases for shallow cumulus and stratus clouds over the trade wind boundary layer; (4) quantify biases in surface wind stress and their potential connection to upper ocean circulation and simulated sea surface temperature (SSTs); and (5) evaluate the abovementioned coupling in terms of climatology and seasonality in the present day and under a warming climate, when including or excluding the radiative effects of precipitating hydrometeors.

This proposal takes advantage of GISS-E3’s advanced cloud microphysics scheme with prognostic falling ice, as opposed to diagnostic one in CESM1-CAM5. A novelty of this proposal is that it extends the analysis of cloud-radiation-circulation coupling to the upper ocean and low-level clouds. An expected outcome from gaining a better understanding of cloud-radiation-circulation coupling is to directly inform GISS-E3 model developers about the effects and sensitivity to FIREs and in particular the regions in which they are important. This will aid model developers in determining to what extent modifications to existing parameterization schemes are necessary and how they should be prioritized relative to changes in other processes.

Chlorine (Cl) and bromine (Br) are powerful oxidizing and ozone depleting agents in the atmosphere. Recent research has highlighted rising levels of the very-short-lived substances (VSLS) with atmospheric lifetimes of less than half a year and their potential contribution to delay the stratospheric ozone recovery. Tropospheric ozone depletion due to Br has been documented extensively. Reactive chlorine released from VSLS can potentially play an important role in the troposphere as well. Oxidation of ethane (C2H6) and propane (C3H8) by Cl are about 10-100 times faster than that via the hydroxyl radical (OH). Chlorine oxidation is also a minor loss pathway for CH4. The rapidly rising levels of manmade chlorinated VSLS (Cl-VSLS) in the recent years, as a result of fast economic growth in Asia, can have significant impacts on atmospheric O3 and OH through a set of chemical chain reactions after Cl oxidation of C2H6, C3H8 and CH4. The VSLS halogen chemistry and the interaction between manmade Cl-VSLS and the naturally occurring brominated VSLS (Br-VSLS) from oceanic sources also have important implications for chemistry-climate coupling.We propose to the MAP program a 4-year project with the NASA GEOSCCM chemistry climate model to simulate VSLS halogen chemistry in the atmosphere and to assess its impact on key atmospheric constituents. The proposed main tasks are:
• Use NASA and NSF suborbital measurements to derive and improve global emissions, including anthropogenic and natural sources, for four major chlorinated Cl-VSLS source gases, i.e., CH2Cl2, CHCl3, C2Cl4, CH2ClCH2Cl.
• Implement the VSLS chlorine chemistry in the GEOSCCM-GMI, including geographically resolved source gas emissions, the chemical degradation of the source gases, and the wet scavenging of the inorganic products.
• Implement the heterogeneous Br and Cl chemistry on particle surfaces in the troposphere.
• Improve the current Br-VSLS emissions scheme in GEOSCCM-GMI to a chlorophyll a based oceanic emissions scheme. This will enable the oceanic bromine emissions to respond to a future climate change.
• Assess the impact of Cl-VSLS and Br-VSLS and their interaction on present-day O3 and future stratospheric O3 recovery.
• Quantify the impact of Cl-VSLS on the atmospheric lifetimes of C2H6, C3H8, CH4, and the atmospheric abundance of key tropospheric trace gases.
• Assess the impact of manmade Cl-VSLS on tropospheric OH abundance and its temporal variations over the past 20 years.

The proposed investigation is directly relevant to the research elements described in ROSES 2020 A.16, Modeling, Analysis and Prediction. The proposed tasks will lead to development of the critical VSLS halogen chemistry in the GEOSCCM with coupled GMI chemistry and its implementation in the offline GMI Chemical Transport Model (CTM). The proposed efforts will utilize NASA observations from various platforms, i.e., ground, suborbital and satellite remote sensing measurements. The proposed efforts address the following research theme called out in A.16 “Constituents in the Climate System: Constituents in the atmosphere (aerosols and chemical species) will respond to climate change, and changes in constituent concentrations can have climatic consequences as well”.

The Earth system models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) will be evaluated against observations for their ability to simulate precipitation extremes with the proper driving meteorological mechanisms over the Continental United States. These models include configurations of the GISS ModelE. Evaluation results will intercompare ModelE with a large suite of state-of-the-art Earth System Models, highlighting areas where investment in model improvements would be productively spent. The observational foundation developed to carry out the evaluation will be primarily developed using GPM/IMERG satellite-based gridded precipitation measurements and MERRA2 reanalysis. A thorough validation of these reference datasets against other gridded precipitation products, in situ station observations, and other reanalysis products will be executed, to understand and constrain reference dataset uncertainty and to highlight potential targets for MERRA2 improvements. The meteorological mechanisms of focus are: atmospheric rivers, frontal systems, extratropical cyclones, mesoscale convective systems, and tropical cyclones.

This project will result in two products. The first product will provide an observational foundation for use in the model evaluation, and a validation of GPM/IMERG and MERRA2 as reference datasets. Results will highlight where, when, and for which mechanisms the reference datasets are in agreement with other observational and reanalysis products and where uncertainty reduction would benefit future iterations of the underlying models and methodology used to construct MERRA2 and IMERG. The second product will be the model evaluation results, which will systematically assess model skill at simulating precipitation extremes with the proper meteorological mechanisms at each grid point across the Continental United States. This product will highlight where, when, and why future investment in model improvements would benefit CMIP6 simulation fidelity on a model-by-model basis and for the suite as a whole.

Analysis will be summarized at the annual and seasonal time scales, and results assessed at grid point through regional scales. Such granularity will hone in on specific targets for model improvement, better isolating sources of model uncertainty and error. For example, a model may capture a realistic relationship between extreme precipitation and frontal systems across the western United States in winter, but not capture a realistic relationship with atmospheric rivers that often accompany fronts. This would indicate that the model is not properly capturing moisture transport dynamics in this geography for this time of year. Furthermore, since a precipitation extreme can be associated with more than one mechanism, as will likely be the case in many instances, results will target model fidelity at capturing interconnected processes such as fronts, extratropical cyclones, and atmospheric rivers, which often occur in concert. All software developed to objectively identify the mechanisms in observations, reanalysis, and models will be documented and made publicly available to the Earth system science community.

Supercooled liquid stratocumulus clouds are ubiquitous over the Southern Ocean (SO) and are of profound importance to global climate and global climate sensitivity. Coupled Model Intercomparison Project phase 5 (CMIP5) models largely underestimated the occurrence of these clouds, causing large radiative errors and excessive heat uptake by the ocean in model simulations. Early analysis of results from current models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) show that at least some models are now producing more supercooled stratocumulus and have reduced mean radiative errors over the SO, but also have a higher overall climatesensitivity that is, at least in part, likely a consequence of this change. t remains to be seen how well CMIP6 models are now capturing SO stratocumulus. NASA satellite datasets will continue to be a critical resource for both broadly evaluating these models, and more generally, for investigating and improving the parameterization of SO supercooled and mixed-phase clouds in climate models.

We propose here to undertake an analysis of cloud and precipitation properties using observations collected during recent Southern Ocean field campaigns with the objective of characterizing the seasonal and diurnal variability of SO cloud and precipitation properties and their dependence on a variety of dynamical and thermodynamical factors, and to use these characteristics in the evaluation of NASA satellite retrievals and CMIP6 models (specifically the GISS Model-E). The research will focus on both cloud and precipitation phase and stratocumulus microphysical properties.

Wildfire is a major contributor of trace gases and aerosols to the atmosphere. Depending on the land surface conditions and atmospheric stability, wildfires can inject smoke and trace gases into the middle or upper troposphere, penetrating planetary boundary layer (PBL). These extreme events are called pyro-cumulus (pyroCu) or pyro-cumulonimbus (pyroCb), and involve latent heat release through condensation/freezing/deposition. The largest pyroCb events can inject aerosol and trace gases into the lower stratosphere. Recent pyroCb activity in Canada (August 2017) and Australia (2019-2020) produced stratospheric smoke plumes that encircled the globe, similar to moderate volcanic eruptions. These catastrophic wildfire events could be intensified in future climate.

However, all climate models poorly represent wildfire plume injection height (PIH), especially pyroCb activity. GISS ModelE is no exception; although it has recently developed interactive fire emission module, pyrE, injection is limited to the lowest atmospheric layers regardless of the intensity of wildfire, and injection is prescribed from offline estimates, which are not well-suited to extreme events. This inability of PIH overall results in poor representation of atmospheric composites in the Earth system model.

The first goal of this proposal is to validate and improve wildfire PIH parameterization through the development of a NASA pyroconvection Development Testbed (pyrDT). The pyrDT will be built upon multi-scale modeling and observation framework in order to
1. simulate pyro-convection (dry plume, pyroCu, and pyroCb) through a process model and single-column model (SCM) by meteorological and wildfire forcing from different climate regimes;
2. validate simulated wildfire PIH and physical processes through observations; and
3. continuously and systematically improve physically based plume-injection parametrizations for Earth system modeling and climate community.

The second goal of this proposal is to apply a validated and improved physically-based PIH parameterization to the aerosol-chemistry-coupled global ModelE simulations to extend the range of model validity in response to recent devastating wildfire events, and exploit the new GISS modelE sensitivity simulations to address following science questions:1. What are the relative roles of atmospheric water vapor, entrainment, cloud nucleation processes and wildfire-driven excessive turbulent heat flux in controlling the magnitude of pyroCb?2. What wildfire and storm environment distinguish volcano-like pyroCb events from ordinary pyroCb or pyroCu? 3. Do pyroCb-driven plume injections rival those from infrequent volcanic eruptions in terms of stratospheric aerosol amount?

These scientific questions are fundamentally linked to those of NASA’s Science Mission Directorate (SMD) with respect to the better prediction and characterization of past/current/future Earth systems. This project focuses on categories: “Constituents in the Climate System” as primary, and “Extremes in the Earth System” as secondary research themes.

Cloud observations from active spaceborne instruments, including CloudSat Cloud Profiling Radar (CPR) and GPM Dual-frequency Precipitation Radar (DPR), present a valuable source of information on the vertical structure of the clouds. However, due to limitations in radiative transfer (RT) scattering calculations and also lack of RT essential inputs provided by the Numerical Weather Prediction (NWP) models, the assimilation of these measurements has been very limited. Over the last decade, the parameterization of clouds in the NWP models has greatly improved, therefore it is expected that the assimilation of cloud-contaminated observations, including radar measurements, can potentially improve the analyses and forecasts. The propagation of the electromagnetic signals in the atmosphere in the presence of clouds is highly affected by the size distribution and the shape of the hydrometeors in the clouds. Therefore, the assimilation of the radar measurements highly depends on the characterization of these parameters, but these parameters are neither accurately measured nor provided by the models. In addition, the assimilation of measurements from active spaceborne radars is essentially more complicated than the assimilation of other satellite observations as active spaceborne radars have a narrow-track horizontal field of view.

We propose to compare the direct assimilation of measurements of CloudSat CPR and GPM DPR using an RT model with indirect assimilation using a 1D+4D-Var assimilation method. In the direct technique, a forward operator along with its adjoint and tangent linear for radar reflectivities will be implemented into the Community Radiative Transfer Model (CRTM). In the indirect assimilation method, the profiles of atmospheric humidity, as well as rain and cloud water content are first retrieved using a Bayesian Monte Carlo Integration (BMCI) technique, then the retrieved humidity and hydrometeor profiles are assimilated through the GEOS hybrid Ensemble 4D-Var system. In both cases, the impact of assimilation of radar measurements into the NASA GEOS model will be evaluated using global skills such as anomaly correlation, observation impact through model adjoint, as well as the impact on track, intensity, and structure of several hurricanes.

The assimilation of narrow-track observations of CloudSat highly depends on the resolution of the NWP model; it is generally expected that the higher the resolution the larger the impact on the state variables. The highest horizontal resolution supported by the GEOS data assimilation system is currently C720 (roughly 12.5 km), but the C1440 resolution (about 7 km) is also under final testing and evaluation. Therefore, we investigate the impact of the model resolution on the assimilation of radar active observations using these two resolutions. Given recent advances in developing Joint Effort for Data assimilation Integration (JEDI), this study uses the JEDI platform for conducting the data assimilation experiments.

We have two objectives: (1) to improve the assimilation of low processing level satellite data, such as L1 satellite radiances in the NASA coupled Earth system model, and (2) to advance from weakly coupled to strongly coupled data assimilation to support the development of an Integrated Earth System Analysis (IESA) capability with GEOS. We focus on the atmosphere-ocean-ice coupling in GEOS, and target prediction scales from sub-seasonal to seasonal (S2S). We aim to address outstanding coupled data assimilation issues and methods for assimilating new lower-processed NASA ocean observations that are not currently assimilated in NASA data assimilation systems.

This proposal investigates the impact of varying levels of processing in assimilated satellite ocean observations (L1,L2,L3,L4) on the coupled analysis in GEOS. Further, we follow the experiment regimen Penny et al. (2019) to evaluate the transition from weakly coupled to strongly coupled data assimilation, applied to GEOS. This includes variations to the observing network, data assimilation method, assimilation frequency, and other parameters in the coupled data assimilation design.

This project directly addresses the long-term goal of the MAP program to develop an Integrated Earth System Analysis (IESA) capability. It also aims at addressing outstanding assimilation issues and methods for assimilating new NASA observations that are not currently assimilated in NASA data assimilation systems - namely the assimilation of L1 satellite ocean observations.

Extratropical cyclones (ETCs) have a significant influence on Earth’s weather and climate, affecting the radiative balance via the low and high clouds they produce, and generating most of the precipitation that falls in middle and high latitudes. Simulations of ETCs are sensitive to the parameterization of clouds and cloud processes, as well as to the characteristics of the ocean surface. In addition, precipitation at high latitudes is thought to be driven by the microphysics of mixed phase clouds, and snow melting into the ocean has a potentially significant effect on the climate system. While there have been several studies of extratropical cyclone clouds and precipitation, the fraction of liquid vs frozen cloud and precipitation, their influence on the radiation budget, and their relationships to the properties of the surface and environment, are still poorly known. It is also not clear whether the representation of liquid and ice clouds in ETCs is well represented in global models.

NASA has made systematic observations of clouds and precipitation in ETCs and has invested in the development of robust front and ETC detection techniques. These observations consist of both the horizontal extent of clouds, and also their interior structure. The Goddard Earth Observing System (GEOS) model is at the center of NASA's observation-driven modeling and assimilation enterprise. GEOS has been shown to robustly reproduce key features of the hydrologic cycle, as well as the horizontal and vertical structure in clouds. However, the partition between liquid and ice clouds in modeled ETCs has not been studied. In addition, the GEOS microphysical scheme has recently been upgraded to include prognostic rain and snow, as well as riming processes. There is a need for quantification of uncertainty in the new parameterizations, and for diagnosis of model performance measured against proven metrics that target cloud and precipitation processes.

In this research, we use proven front and cyclone identification and compositing techniques to study the incidence of liquid and ice clouds and precipitation in high latitude extratropical cyclones. NASA observations of liquid and ice clouds and precipitation will be composited, then subset according to storm dynamics and environment to study ETC evolution and processes. We will evaluate clouds in ETCs in MERRA-2, and will compare them with the newly upgraded GEOS model, targeting processes in model’s new mixed phase microphysical scheme. We will use proven numerical sensitivity analysis techniques to evaluate uncertainty in the GEOS microphysical parameterization, perturbing both the model parameters and microphysical process rates. We will compare the microphysical scheme sensitivity with sensitivity to changes in the environment using proven ensemble generation techniques. We will implement estimates of ocean cooling due to melting snow into the GEOS ocean model. Quantities of interest in observations and output from the model include: liquid and ice cloud frequency of occurrence, the vertical distribution and total amounts of liquid and ice, rain and snowfall frequency and precipitation rates, radiative fluxes, and cooling due to snow melting into the ocean.

The numerical experiments will be hierarchical, consisting of: (1) a large number of single column model simulations of frontal regions to quantify sensitivity; (2) a smaller number of short-duration global model forecasts to verify the SCM sensitivity and to compare model fields with observations, and (3) seasonal forecasts spanning October - April. The outcomes of our research will include: knowledge of high latitude cloud and precipitation structures and covariance with environmental characteristics, and (2) sensitivity analysis of parameters in the GEOS microphysical scheme. This extratropical cyclone-focused research primarily addresses topic 1) Clouds in Earth System Models, with a secondary focus on 2) Extremes in the Earth System.

The overarching goal of this proposal is to improve the capability of land-atmosphere modeling with the NASA Unified Weather Research and Forecasting Model (NU-WRF) using strongly coupled data assimilation. Strongly coupled data assimilation can estimate cross-model error covariance; thus, it enables the correction of land surface and atmospheric variables simultaneously and results in improved land-atmosphere coupling with consistency between land and atmosphere. Previous studies from the PI’s group have proved that a strongly coupled land-atmosphere outperforms the traditional weakly coupled method in numerical weather prediction. With this proposed project, we will:• Implement strongly coupled land-atmosphere data assimilation with NU-WRF and make it part of the NASA NU-WRF data assimilation options for the research community, along with the NASA Land Information System (LIS).• Examine the influence of strongly coupled land-atmosphere data assimilation on accurate numerical simulations of extreme weather systems.• Understand the impact of coupled land-atmosphere data assimilation on improved representation of near-surface and boundary layer atmospheric conditions. • Study the processes associated with land-atmospheric interaction and the evolution of atmospheric boundary layer structures during extreme weather events.

We propose to achieve the above research objectives by assimilating in-situ and satellite soil moisture retrievals (such as those from SMAP, SMOS, and other sources, e.g., CYGNSS), snowpack, and skin temperature into NU-WRF while also updating land parameters, in conjunction with near-surface atmospheric observations and other conventional and satellite weather observations. The extreme weather systems to be used for case studies will be 1) flood-inducing heavy rainfall over the U.S. Great Plains, 2) strong wind and precipitation caused by inland evolution of landfalling hurricanes, and 3) winter weather over the Intermountain West, in which land-atmosphere interactions are essential for accurate prediction and numerical simulations.

The project addresses core science questions related to significant components of the NASA MAP research themes “Coupling in Earth System,” “Assimilation,” and “Extremes in Earth System.” The outcomes should also be beneficial to the NASA modeling system for improved coupled data assimilation and weather/climate prediction capabilities.

Fire is a fundamental part of the Earth system, altering ecosystem structure and composition, atmospheric aerosols and chemistry, and greenhouse gas concentrations. Yet, the role of fire in many ecosystems is changing rapidly in response to climate warming and human management. In temperate and boreal ecosystems, climate change has created new extremes in fire weather that allow fires to burn hotter, faster, and longer, creating unprecedented ecosystem impacts. In the past five years, fast-moving fire storms in the United States, Australia, Portugal, Canada, and Russia have killed billions of trees, forced the evacuation of tens of thousands of people, damaged or destroyed many communities, and have severely reduced air quality in many regions. Smoke from the largest and most intense fires has reached the stratosphere, circling the globe, altering atmospheric chemistry and radiation budgets for an entire hemisphere over periods of weeks to months. Understanding and predicting these extreme fire events require new tools.

Here, we propose two parallel lines of research to improve the representation of fire emissions in the GEOS forecasting system on daily and seasonal timescales. First, we will develop a system to track individual large fire events at a global scale in near-real time using VIIRS active fire detections and forecast fire spread and emissions using outputs from the GEOS-FP system. This effort will provide the scientific basis for dynamic emissions forecasts, in contrast to the current approach that holds emissions constant over the 10-day forecast interval. We also propose to build an interactive online tool within GMAO’s FLUID visualization portal to deliver data to users from this new product suite, the Global Large Fire Database. This work builds on recent efforts by our team to develop the Global Fire Atlas of large fire events (Andela et al., 2019), cluster VIIRS active fire detections to classify individual events by fire type (Andela et al., 2020), and forecast individual fire spread (Graff et al., 2020). By coupling emissions from these events with the GEOS-CF system in several case studies, our approach will provide new information about the near-real time changes in regional air quality and global atmospheric composition from individual extreme fire events. Linking fire behavior (and fire spread) with emissions and atmospheric composition has been an outstanding grand challenge in fire science for over a decade, and our proposed work takes an important step toward bridging these two disciplines at a global scale.

Second, we will build a subseasonal-to-seasonal (S2S) global fire emissions forecasting system for integration into the GMAO's GEOS-S2S framework. Our team has built statistical models for seasonal forecasts of fire season severity (Chen et al., 2011; 2016) and developed new autoregressive statistical models for sub-seasonal forecasts of fire emissions (Chen et al., 2020). Here, we propose to extend these forecasting capabilities using predictions from the GEOS-S2S system. Such a system is needed for interpreting new extremes in fire season severity, for forecasting the response of greenhouse gases to changes in climate, and for guiding fire and air quality management in regions that lack locally-tailored seasonal outlook systems. Together, these activities provide the scientific basis for allowing fire aerosols and trace gases to vary in the GEOS-S2S modeling system.

The proposed research targets the Extremes in the Earth System theme in the A.16 opportunity, with additional relevance to the Predictability in the Earth System and Constituents in the Earth System themes. This work will combine cutting-edge fire science with state-of-the-art modeling tools to forecast the spread of large wildfires and anticipate seasonal conditions that increase the risk of extreme wildfires events.

This project will develop a better understanding of the physical processes governing the structure and evolution of the marine atmospheric boundary layer (MABL) in the Northeastern US and the New England shelf regions. Capitalizing on the detailed in situ and remotely-sensed observations of coupled boundary layer processes and direct covariance measurements of air-sea fluxes uniquely available in the region, this team will validate and improve the MABL processes and air-sea interaction parameterizations in the NASA's Unified WRF (NU-WRF) modeling system. By including the coupling of the regional ocean modeling system (ROMS) and the WaveWatchIII (WW3) wave model to the NU-WRF modeling framework, the project will also enable, for the first time, NU-WRF-based coupled hindcast and forecast capabilities of extreme weather events.

The research team will undertake comprehensive modeling and validation efforts of the planetary boundary layer (PBL) schemes in the NU-WRF model under various MABL conditions, including extreme extratropical cyclones and stable boundary layers. The simulation of turbulent fluxes of momentum, heat, and moisture in the surface layer and their coupling with the PBL will be improved by correctly incorporating the Coupled Ocean-Atmosphere Response Experiment (COARE) bulk flux algorithm to the NU-WRF surface layer physics and directly incorporating ROMS and WW3 into the NU-WRF system. The research team will then carry out a series of case study simulations using this improved and newly coupled NU-WRF model to quantify the impacts of the ocean/wave coupling on the NU-WRF simulation skills of extreme coastal storms.

The project's most significant outcome is to enhance our scientific understanding of air-sea coupling and MABL dynamics in high-resolution models by demonstrating how a better understanding and representation of the MABL processes improves the fidelity and accuracy of weather and climate prediction models. The project will also provide new and improved NU-WRF hindcast/forecast capabilities over the ocean, with differing levels of representation of the ocean and waves coupling options. The improved COARE bulk flux algorithm will be coupled with a variety of available surface layer and PBL schemes within WRF, thus immediately benefiting the broad WRF modeling community

This project is well-aligned with the long-standing goal of the NASA MAP Program to ""understanding of the Earth as a complete, dynamic system."" It is also relevant to the specific research theme included in the 2020 MAP solicitation on ""Coupling in the Earth System"" as the project will advance our understanding and increase our capacity to represent the coupling across the air-sea-wave boundary layer in weather and climate models. Further, our investigation of air-sea interaction and model skills in the NU-WRF under high-impact weather events is relevant to the research theme on ""Extremes in the Earth System"". Currently, NASA has a PBL Study Team to inform NASA PBL science and technology capabilities, as part of the Incubation phase of a PBL observable. The proposed work is highly relevant to science related to incubation observing-system priorities in the 2017 NASA Decadal Survey related to the PBL.

Wildfire is an extreme disturbance of the ecosystem and a recurring hazard in California and other western U.S. states. Severe wildfire not only threatens human life and property, but also has lasting environmental consequences. Wildfire releases large amounts of aerosols, trace gases, and heat into the atmosphere, leading to local precipitation changes caused by perturbations of sensible and latent heat, and alterations in aerosol-cloud-radiation interactions. Simultaneously, wildfire abruptly alters the thermal and hydraulic properties of the underlying land use/land cover (LULC) and subsurface soil, resulting in changes in land-atmosphere exchange of heat, momentum, and water. This in turn leads to a cascading effect on planetary boundary layer (PBL) circulation and precipitation at local to regional scales. Collectively, wildfire induced changes in LULC and precipitation favor an increase of storm-water runoff, posing serious flooding threats over the burnt areas and over land in the downstream. With the increasing trend of wildfire outbreaks in line with more frequent drought in the western U.S., quantitative assessment of the impact of wildfire on local-regional water cycle becomes essential for a better understanding of the role of fire in the climate system. However, in-depth studies on the issue are limited.

In this proposed investigation, we plan to use the high-resolution NASA Unified Weather Research & Forecasting (NU-WRF) regional modeling system, and observations from satellite and other platforms to answer the overarching science question of “How will wildfire change the local to regional water cycle through modification of aerosol emissions and perturbation of LULC?” Taking the 2018 wildfires in California and Nevada as examples, the following specific questions will be addressed:1) What are the immediate effects of wildfires on changes in local/regional precipitation and surface runoff?2) What are the lasting effects of wildfires on land-atmosphere exchange, PBL circulation, and catchment to regional water cycle?

The first question will be addressed focusing on the role of aerosols released from wildfires on convection and aerosol-cloud-radiation interactions. A two-moment cloud microphysics scheme will be implemented and coupled with the aerosol module in NU-WRF to better simulate the aerosol-cloud interactions. The model’s ability to simulate fire-precipitation-runoff processes will be evaluated against observations from satellite and other platforms. The relative roles of land surface change and fire aerosols on the hydrological responses to wildfire will be elucidated through NU-WRF experiments with perturbations.

The second question will be answered focusing on fire-induced changes in LULC and their associated thermal and hydraulic properties, such as surface albedo, emissivity, roughness, and soil infiltration/hydrophobicity. Changes in biogenic emissions and leaf area index tied to LULC will also be taken into account. A new LULC type – burnt vegetation – will be developed using data from MODIS, Landsat sensors, and other platforms. This new LULC will then be integrated in NU-WRF’s Noah-MP land surface model to represent the unique characteristics of a burnt land in the perspective of the Local Land-Atmosphere Coupling (LoCo) between land surface, PBL, and precipitation. The synthetic aperture radar regulated time-varying burnt land properties will be implemented in NU-WRF to reflect the gradual recovery of burn scars. Multi-year NU-WRF simulations will be carried out to quantify the longer-term effects of wildfire on catchment-regional water cycle focusing on precipitation and runoff.

The successful completion of the proposed study will not only deepen the understanding of wildfire impact on the environment, but also enhance NU-WRF’s capability in predicting and analyzing extreme events in nature.

Biomass burning emissions play an important role in regulating both short-lived and long-lived species in the atmosphere. The emissions are not only in the form of gases such as CO2, CO but also in the form of particles such as black carbon (BC) and organic carbon (OC) aerosols. As a result, accurate estimates of biomass burning emission amounts and their chemical speciation are equally critical for the forecast of chemical weather and understanding of atmospheric chemistry and climate interactions.

Depending on the region and time of interest, existing inventories of biomass burning emissions for carbonaceous (BC or OC) aerosols can have uncertainty up to a factor of 10. One key source of this uncertainty is fire emission factors that depend on the surface type and combustion efficiency. For the same surface type or the same fire, the combustion efficiency may also vary with time depending on the meteorological conditions and other environmental factors. However, even in the top-down estimates of biomass burning emissions in which the total amount of fuel burned is constrained by the fire radiative power (FRP) measured by the satellite, the temporal variation of emission factors as a function of combustion efficiency are largely ignored. In many methods for inventory estimates, the emission factors are prescribed for 6-10 surface types and are used as constants for each season if not year round.

Here, we propose a novel approach to improve the estimate of fire emission factors by using satellite-based fire visible energy fraction (VEF) data that we are able to retrieve from Visible Infrared Imaging Radiometer Suite (VIIRS) at night. VEF is defined as the ratio between the fire radiative power in the visible spectrum and the total FRP and, therefore, is an indicator of fire combustion efficiency: the larger the VEF, the more complete the combustion. Building upon this approach, we will conduct the subpixel characterization of fire flaming and smoldering fraction by using Moderate Resolution Imaging Spectroradiometer (MODIS) channels centered at 3.70, 4.05 (M-13, dual gain), 8.55, 10.76, and 12.01 m, while similar bands can be found at VIIRS as well. By doing so, we will derive the modified combustion efficiency (MCE) from MODIS and VIIRS for fires at both day and night and, consequently, improve Quick Fire Emission Dataset (QFED) that drives NASA’s Goddard Earth Observing System (GEOS) forecasts and reanalysis of fire-emitted species in the atmosphere. Specifically, the following three tasks will be conducted to improve the QFED estimates of fire MCE and emission factors for CO, BC, and OC aerosols in GEOS: • Use the established relationship between VIIRS-based VEF and MCE to derive the spatial and temporal variation of emission factors (EF) from VIIRS, thereby improving the biomass burning emission species for fires at night. • Characterize the fire flaming and smoldering fraction and consequently derive VEF in each MODIS and VIIRS active fire pixel with a fast bi-modal approach for both day and night. • Improve QFED by considering the variation of MCE and emission factors based on the analysis of VEF from both VIIRS and MODIS, and evaluate such improvement by comparing GEOS simulations with the data collected during the CAMP2EX, ORACLES, and FIREX-AQ field campaigns, from the AERONET and IMPROVE observation networks, and from the retrievals of CO and absorption aerosol optical depth from MOPITT and OMI, respectively.

This proposal specifically addresses the research area of “Constituents in the Climate System” in the MAP solicitation that encourages “proposals that address emissions parameterizations”. The proposed work also has applications for the research area of “Extremes in the Earth System” in the current MAP solicitation that calls for proposals to study “the degree to which these phenomena” (extremes such as wildfires) “and their impacts are properly represented in Earth system models”.

Forests are highly vulnerable to extreme events, such as droughts, heat waves, and fires. These extreme events can trigger tree mortality, change forest structure and composition, alter forest successional trajectory, and even cause regime shifts in forest ecosystems. Empirical studies have found that, in response to these extreme events in recent years, tree mortality rates have increased, tree sizes have shrunk, and forest species composition has shifted to more opportunistic species (e.g., evergreen to deciduous in boreal and tropical regions) globally. Accurate modeling of these responses as well as their feedback onto the climate system is critical for understanding how forest will continue to respond to extreme events in the future, as the frequency and intensity of those events is expected to increase. However, in current Earth system models, global vegetation is represented by only a small number of plant functional types, with physiological and demographic parameters obtained from a few dominant plant species in each biome. The functional diversity and key physiological and demographic processes, such as plant hydraulics, mortality, regeneration, and compositional shifts, that are critical for predicting forest responses to climate extremes are not well represented.

In this four-year research project, we will: 1) develop a parsimonious plant hydraulics and mortality model that simulates plant physiological performance and mortality risks for investigating global forest responses to climate extremes; 2) assimilate data from NASA satellites and sensors (e.g. OCO-2, ICESat-2, and GEDI) with data assimilation and machine learning approaches to quantify model uncertainty and improve model predictions of vegetation dynamics; 3) incorporate it into the newly developed vegetation demographic model (BiomeE) in NASA Goddard Institute for Space studies’ Earth system model, ModelE (GISS-E) to investigate how climate extremes affect demographic processes, e.g., reproduction, growth, and mortality, and successional changes in vegetation structure and composition with a focus on drought and heat waves; 4) couple this model into GISS-E to predict long-term forest structural responses to climate extremes, and investigate vegetation-climate interactions.

This proposal is directly responsive to the goals and programmatic priorities outlined in the MAP 2020 solicitation, aligned with the “Extremes in the Earth System” research theme. The main output from this proposal will be a new vegetation model coupled with GISS-E, data assimilation and machine learning algorithms for critical vegetation processes, and uncertainty analysis approaches that can be used by the Earth system research community. It will advance the modeling of forest ecosystems in GISS-E and improve its predictions of terrestrial carbon and water cycles in a changing climate.

As a common boundary layer phenomenon over the polar regions, blowing snow (BLSN) redistributes surface mass and drives spatial and temporal variations in snow accumulation. Adding a BLSN component to the Goddard Earth Observing System (GEOS) model output will significantly improve its capability as a dataset for studies on polar surface mass balance (SMB), radiation budget, boundary layer humidity, and coupled ice-atmosphere system.

Building on our team’s long-term experience with BLSN product development and analysis, we propose to deliver an algorithm package for the GEOS system that provides diagnosis on BLSN properties over the Antarctic and Greenland ice sheets. To achieve the goal, we will adopt a machine learning approach with observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) for training. The proposed work will include the following main activities: (1) develop, test and validate the machine learning models for the prediction of BLSN occurrence, height, optical depth, column integrated sublimation, and mass flux; (2) apply the machine learning algorithm package to the current GEOS forward processing (FP) outputs and to the MERRA2 data record for generating GEOS BLSN products; (3) conduct studies on the impact of BLSN on the SMB of the Antarctic and Greenland ice sheets with the newly developed MERRA2 BLSN data record.

The proposed work will expand the GEOS capability to include the near real-time diagnosis of a very important constituent of the weather and climate system, namely BLSN. It directly responds to the solicitation for (a) studies to expand our understanding of the role of atmospheric constituents in the context of the climate system, and (b) investigations leading to an improved understanding and representation of the interactions between different Earth system components and their influence on boundary layer structure and function.