NASA's Science Mission Directorate, NASA Headquarters, Washington, DC, has selected proposals for the Modeling, Analysis and Prediction (MAP) Program in support of the Earth Science Division (ESD). The Modeling, Analysis, and Prediction (MAP) program seeks an understanding of the Earth as a complete, dynamic system, with particular emphasis on climate and weather. The MAP program supports observation driven modeling that integrates the research activities in NASA’s Earth Science Program.
The research is distinguished by rigorous examination and incorporation of satellite-based observations, using models to bridge the spatial and temporal scales between satellite observations and observations from ground and air based campaigns. This contributes to the validation of the satellite observations and to observationally based improvements of Earth system model components. MAP strives to generate models and model components that are documented and validated.
The research themes specific to this solicitation include investigations to improve the understanding and representation in Earth system models of clouds, extremes, constituents, coupling, and predictability, as well as data assimilation and advanced analysis methods for model evaluation. The ESD has selected 38 out of a total of 161 proposals received in response to this solicitation. The total funding for these four-year investigations is approximately $35 million USD.
GCM studies have shown that choices in the representation of cloud and precipitation microphysics in convective updrafts can impact the climate sensitivity of a GCM (Mauritsen and Stevens 2015, Zhao 2014, Tan et al. 2016). Mixed-phase microphysical processes in deep convection produce lightning and associated nitrogen oxides (NOx) that strongly impact pollutant and reactive greenhouse gas abundances, but changes in a warming world are far from certain (Murray, 2016). Recent airborne measurements of ice particle size distributions in deep tropical convection indicate a prominent mode of outflow ice at sizes of a few hundred micrometers (Fridlind et al. 2015), found to require substantial ice formation at relatively warm temperatures neighborhood (around -10 deg C) and favored by an efficient warm rain process (Ackerman et al. 2015).
We propose to investigate these issues through three components. First will be to exploit the unique airborne measurements obtained within the cores of tropical maritime deep convection through the High Altitude Ice Crystals / High Ice Water Content (HAIC/HIWC) campaigns flown out of Darwin, Australia and Cayenne, French Guiana (Leroy et al. 2015). We will use in situ measurements and airborne W-band radar retrievals together with surface-based C-band polarimetric (C-POL) radar retrievals to constrain poorly quantified warm ice production processes in a set of NU-WRF simulations with two-moment microphysics (Morrison et al. 2009) by targeting a mesoscale convective system case study sampled within range of C-POL.
The second component will be to adapt the Morrison two-moment microphysics scheme within the ModelE moist convection framework of Del Genio et al. (2005, 2007, 2015), which is cast in terms of two updraft parcels with updraft speeds computed assuming different entrainment rates, and condensate is diagnosed as either liquid or ice based on a specified temperature dependence. We will use the updraft parcels to populate a persistent convective plume of hydrometeors that interact with subsequent parcels, allowing for a less preordained, more organic representation of parcel microphysics suitable for a two-moment scheme. The plume microphysics model will be informed and constrained by the observations and simulations from the first component of the proposed work.
The third component will start with implementation of a cloud electrification scheme that builds on the mixed-phase processes represented by the new convective microphysics model. Development and evaluation of the lightning scheme will use the 17-year satellite record of lightning, precipitation, and cloud properties observed by the Lightning Imaging Sensor (LIS) and fellow sensors on the NASA Tropical Rainfall Measurement Mission (TRMM, 1997-2015). The new lightning mechanism should improve our ability to represent present-day lightning distributions in ModelE, as well as offer additional constraints for convective cloud processes.
We propose a focused study of high-resolution Goddard Earth Observing System (GEOS) modeling, validation, and analysis to investigate atmospheric waves and their influences on winds in the upper troposphere and stratosphere. Winds at these levels guide Rossby wave propagation and teleconnection patterns that strongly influence the simulation of regional-scale climate patterns and skill of long-range weather forecasts. Most climate and weather forecasting centers have raised their model lids in recognition of the importance of these upper level winds and the importance of simulating the processes that control them. At seasonal forecast model resolutions, small-scale waves remain severely under-resolved, yet the influence of their drag forces on the circulation in the upper troposphere and stratosphere make them a key player in predictability. Gravity wave drag parameterizations that treat orographic and non-orographic waves are used to tune both climate and forecast models with demonstrated effects on bias reduction and forecast skill. This project will inform improved methods for simulating small-scale wave effects on two important features of the circulation with demonstrated influence on seasonal predictability: The tropical lower stratosphere and the winter season stratospheric vortex. This puts our focus on orographic gravity waves and those generated by localized intense rain events.
The NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 model is designed for simulation at a wide range of atmospheric resolutions: For example, it is currently configured for climate simulations with ˜100 km resolution, and specialized hindcasting experiments are routinely run in the "gray zone" (for both deep convection and gravity waves) at ˜6 km. Vertical resolution is currently 72 levels with efforts to double that underway. Atmospheric waves and their interaction with global and regional circulation are quite sensitive to these resolution choices, but also sensitive to moist processes, surface drag, divergence damping, and other dissipation mechanisms. As a result, resolution alone dictates neither the scales of the waves that can be resolved nor other wave properties including their sources, geographic distributions, and drag on the circulation. For example, previous high-resolution experiments, like the 7-km resolution "Nature Run", needed strong parameterized gravity waves to counteract the effects of explicit plus implicit dissipation on waves resolved in the model.
The project approach includes (i) observational validation of gravity waves and small-scale heavy precipitation events in existing and future high-resolution simulations with different dissipation settings, (ii) exploratory limited-area high-resolution GEOS experiments to probe effects of different physics, dissipation, and resolution settings on small-scale resolved gravity waves and latent heating sources, (iii) an update to the existing orographic gravity wave drag parameterization to include effects of anisotropy and low-level wave trapping, and (iv) analysis of validated global model experiments to infer the roles of small-scale waves on major modes of variability and on wind biases. The work is expected to result in a dramatic improvement in understanding of the precise roles of small-scale waves on circulation, and will also inform the GMAO's planned FY 2019 tests of a new prototype GEOS model through understanding of a variety of model setting choices on small-scale waves and grid-scale precipitation.
Objectives: The objectives of this proposal are to: (i) Use satellite observations to evaluate critical aspects of clouds, convection and radiative feedbacks in GEOS-5, including their spatial distribution, co-variation, and diurnal and intraseasonal variability; (ii) Implement a series of parameterization changes targeting model deficiencies, including a new cold pool parameterization, and variations of entrainment and rain evaporation rates; (iii) Identify how these model changes affect simulated clouds and convection at the process level.
Motivation: Feedbacks among convection, moisture and radiation are important drivers of tropical variability, but their representation in climate and forecast models remains inadequate. Models have particular difficulty reproducing the observed relationship between convective depth and tropospheric humidity, which has been implicated in early diurnal rainfall peak, and weak intraseasonal variability. Past efforts to address these issues have focused on the entrainment rate in deep convection schemes, which has been used to increase convective moisture sensitivity and improve simulation of the Madden-Julian Oscillation. However, this approach often leads to unacceptable mean state biases. The ubiquity of this tradeoff, the "entrainment dilemma," suggests that new physical processes must be included to allow for strong convection in humid regions even while entrainment limits convection in dry regions.
Cold pools and related sub-grid "organization" have recently emerged as a possible solution. Cold pools are generated from convective downdrafts, and as they spread, can trigger and organize new convection through mechanical lifting and thermodynamic effects. The enhancement this provides in regions of strong convection may allow high entrainment rates to provide sensitivity to humidity without overly limiting convection in humid regions. In addition, cold pools offer a source of memory to convection, which may improve the simulated diurnal cycle.
Technical Approach: We will use a wide range of satellite data, including cloud water content, relative humidity, precipitation, and radiative fluxes, to evaluate clouds and convection in the NASA GEOS-5 model. We will use conditional sampling techniques to assess the covariation of cloud-related quantities with large-scale thermodynamic conditions. The simulated diurnal cycle and intraseasonal variability will be evaluated against satellite data, and heat and moisture budgets used to understand the role of individual physical processes in model deficiencies.
We will then conduct a series of parameterization changes, re-evaluating the above cloud and convection metrics for each experiment. A cold pool parameterization will be implemented, and experiments conducted with several coupling options with the deep convection parameterization. Parameter variation experiments will be used to understand the effects of convective entrainment and rain evaporation, and identify an optimal balance of processes. The effects of new shallow and deep convection schemes will also be studied.
Relevance to NASA and MAP: This proposal addresses the MAP16 key research theme of "Clouds in Earth System Models," and the programmatic priority of "characterizing the limits of validity of models and model components." By implementing a cold pool parameterization in the GEOS model and targeting known model deficiencies in diurnal and intraseasonal variability, the proposed work will enhance the capabilities of the GMAO, one of the two functional organizations funded by MAP.
This proposed project focuses on the extreme conditions over the Sahara that give rise to the hot, dry, and dusty air mass known as the Saharan air layer (SAL). The SAL arises from the strong surface heating over the Sahara that generates deep (surface to ˜500 hPa) dry convective mixing. The SAL is critical to the formation of the African Easterly Jet and African Easterly Waves, both of which profoundly impact the weather of northern Africa and also the formation and evolution of many Atlantic hurricanes. Observations of this region are relatively scarce, and variability of the SAL has not been well characterized, especially on smaller spatial and temporal scales. This research effort focuses on addressing several science questions related to the SAL:
These questions will be addressed through the following research tasks that make use of several MERRA-2 data sets, high-resolution NU-WRF simulations, and observations from CALIPSO, MODIS, north African rawinsondes, and NASA airborne observations from the HS3 and NAMMA field campaigns.
This study will also fill a thin spot in the literature on the diurnal cycle of the SAL and its evolution on very fine spatial and temporal scales. Finally, it will provide an opportunity to reassess the findings of Braun (2010) related to the impact of the SAL on hurricanes by providing a much higher resolution product, and one with aerosol information, compared to his study using the coarser resolution NCEP Final analyses with no aerosol information. Our project focus on the SAL and its interactions with hurricanes has primary relevance to the MAP solicitation element related to extreme weather, but also has relevance to the "Constituents in the Climate System" and "Coupling in the Earth System" themes.
We propose a four-year modeling and analysis project to investigate the process of aerosol-cloud-radiation interactions (ACRI) in the context of climate change, and to understand the effects of such interactions on downward solar radiation trends at the surface (RSFC) in the past three decades. Our objectives are:
The proposed project is motivated in part by the results of our previous MAP project, which examined aerosol trends and their effects on solar dimming/brightening. Our results indicated that the role of the aerosol direct radiation effect is rather limited compared to clouds, but we were unable to address aerosol effects on clouds due to limitations in the methodology. Several key questions remain: a) What causes the cloud trends? b) To what degree is cloud variation due to aerosol-cloud-radiation interaction? and c) What is the effect of climate change/variability on the change of clouds? These questions will be tackled in this proposal with new modeling capabilities and more extensive observations.
We will use the GEOS-5 atmospheric general circulation model (AGCM) as a major modeling tool, where included in aerosols are dust, sea salt, black carbon, organics, sulfate, nitrate, and ammonium. We will implement an observation-based parameterization of secondary organic aerosol (SOA) and primary biological aerosol particles (PBAP), which are both potentially important sources of cloud condensation nuclei (CCN) or ice nuclei (IN) but are currently not yet considered in GEOS-5. The most recent version of GEOS-5 embraces a new cloud microphysics scheme, which has not only significantly improved the representation of liquid and ice clouds but has also made it possible to model aerosol-cloud interactions within the GEOS-5 framework.
Specifically, our proposed activities include:
The outcome of the proposed project will provide critical answers to the fundamental question of how clouds respond to ACRI in the climate system and the role aerosol play in dimming/brightening and will help project the surface radiation budget in future climate change scenarios with increased confidence. Although our proposal particularly fits the MAP research theme of "Constituents in the Climate System", it also addresses the "Clouds in Earth System Models"theme and relevant to "Predictability in the Earth System".
Droughts and heat waves are extreme events that occur regularly in many regions, causing significant and often persistent disruptions to agriculture, human health, and ecosystems. Recent notable events with significant consequence for ecosystems and societies include heat waves in Europe in 2003 and Russia in 2010, droughts in the Levant and Central Plains, and the ongoing, multi-year drought in California. Because the impacts of droughts and heat waves are so large, there is interest in improving our understanding of how climate change will affect these extremes. But to improve our confidence in these model projections, we need better evaluations of how well models simulate these extremes and improved constraints on the process based uncertainties related to natural sources of variability, including sea surface temperatures (SST), internal (unforced) atmospheric dynamics, and land-atmosphere feedbacks.
The primary goal of this proposal is to improve our understanding of the variability and physical drivers of droughts and heat waves in the Northern Hemisphere extra-tropics using observations and new simulations of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) "ModelE". Using ModelE, one of NASA's core modeling efforts, and NASA observations and reanalyses, we will investigate natural and anthropogenic contributions to drought and heat wave dynamics during the historical period and in the coming decades. Our specific research objectives are to:
We will use indices and definitions of heat waves and droughts that can easily be translated between observations and ModelE, and leverage some of the unique capabilities of ModelE to analyze interactions between these extremes and under-explored processes in the Earth system (irrigation and fire). This work will result in an improved understanding of the natural climate variations driving heat and drought extremes, and better confidence in our ability to use climate models to investigate these events.
Wind-driven waves at the ocean surface represent an understudied component of the Earth system affecting air-sea exchange and boundary layer processes with cumulative effects across all scales. Through tightly interdependent processes the interactions between wind and waves impact air-sea exchange of mass, heat and momentum. These processes have major implications for local, regional and global chemistry, atmospheric boundary layer structure, cloud microphysics, and radiative transfer thereby impacting the composition and structure of the marine troposphere. The ability to accurately capture these processes and collectively account for their impact on local, regional and global climate and climate extremes requires that the ocean wind-wave field be modeled explicitly. However, while software technology and computational power are permitting much better spatial and physical resolution of the Earth system, the NASA GEOS Earth System Model (ESM) still parameterizes key processes such as marine aerosol production, heat and momentum transfer between atmosphere and ocean in highly simplified manner.
Motivated by the need to explicitly resolve the air-sea interface in the GEOS system, we propose to implement a reliable spectrally resolved wind-wave component into the GEOS-5 to permit physically-based coupling between the ocean and atmosphere through wind-wave processes. The proposed research focuses on: Accuracy of the wind-wave field within the GEOS-5, driven with reanalysis meteorology; production of primary marine aerosol (PMA); associated impact of sea spray on latent and sensible heat exchange, and impact of dynamically-linked ocean surface roughness on the marine boundary layer.
The proposed study will be executed over four years with the following objectives:
Incorporation of explicit wind-wave physics will permit the study of a broad range of otherwise neglected Earth system feedbacks that nonetheless become cumulatively more important as Earth System Models (ESMs) reach finer spatial resolution. The proposed work will ensure that processes (e.g., radiation, clouds, biogeochemistry) affected by these feedbacks can adequately account for them. Improvements to the GEOS modeling system are manifold:
The proposed research contributes to the MAP program goal to develop an understanding of the Earth as a complete, dynamic system. The development of new parameterization of air-sea exchange for primary marine aerosols directly responds to the goals outlined in the Constituents in the Climate System theme, and the implementation of coupled wind-wave interface directly responds to the Coupling in the Earth System and Predictability in the Earth system themes.
Accurate model simulation of the effects of biomass burning on the chemical and climate system depends on the emissions estimates, in the representation of the underlying, 'core', climate processes, and in the representation of chemical processes. Changes in a model's cloud scheme, for example, will affect the vertical transport and distribution of emissions, the chemical processing of those emissions via changes in moisture and cloud cover, and the wet removal of soluble species. When a model uses a prognostic fire simulator, the flammability and therefore the predicted biomass burning emissions will also depend strongly on the model's cloud processes.
Understanding and reducing model-observation discrepancies in atmospheric composition has focused largely on biases in prescribed biomass burning emissions and the representation of chemical processes. Little effort has been spent understanding the contribution of core climate process uncertainty to discrepancies in atmospheric composition. To the best of our knowledge, this has not been examined systematically alongside uncertainties in emissions and chemical representations. Based on our recent work, core model physics changes can indeed have effects comparable to emissions or chemistry on simulated atmospheric composition. Because of this, we argue that changes to emissions or chemistry schemes that produce an apparent improvement in performance could actually be compensating for errors elsewhere in the model. Consequently, we argue that model-based estimates of the effects of biomass burning on the chemical and climate system – rainfall patterns, for example - are mostly model-dependent, making it difficult to draw robust conclusions. This is especially the case as models become more sophisticated, which is always the case with ModelE.
To address this problem, we propose to conduct Perturbed Physics Ensembles (PPEs) with the NASA GISS ModelE coupled chemistry-climate model. As part of proposed work, we will enhance the current ModelE fire simulator and implement a simple injection height model. PPEs will be conducted across the prognostic fire, injection height, subgrid atmospheric physics and composition components of ModelE. PPE members will be evaluated jointly against a representative set of composition and climate observations. A reduced set of permissible PPE members will be used to explore outstanding questions related to the sources of carbonaceous aerosols in biomass burning regions, to mechanistically understand the transport and chemical pathways through which biomass burning affects atmospheric composition and regional climate, and to produce a range of future biomass burning impacts scenarios more comprehensively accounting for model uncertainty.
Fire is a critical component of the Earth system, affecting biogeochemical and hydrological cycles. Fire is also an important source of pollutants, greenhouse gases (GHGs), and aerosols, contributing ˜40% of global total black carbon (BC) and carbon monoxide (CO) emissions. Additionally, aerosols from fire sources account for about 30% of cloud condensation nuclei in the atmosphere. In the land system, fire changes the presence and characteristics of vegetation, which affects the exchanges of water, energy, and momentum with the atmosphere. Changes to the land surface and vegetation additionally impact surface albedo and hence, the Earth radiation budget.
Fire-weather conditions created by the climatic state, coupled with land surface characteristics and vegetation, determine both the occurrence and spread of fires. Fire activity therefore both affects and is affected by the land-atmosphere system. The Arctic boreal zone is an important illustrative example of these types of feedbacks. Fire activity is predicted to increase in the future as temperatures warm in response to climate change. Increases in Arctic fire activity are expected to impact vegetation, hydrology, and radiative forcing. The large carbon reservoir in Arctic soils has led to concerns of its rapid release, which would greatly exacerbate global warming, particularly since permafrost temperatures are rising and fires accelerate permafrost thaw.
Earth System Models (ESMs) are a powerful tool to understand the connections between individual Earth system components and their sensitivities to changing conditions (i.e., climate change). In this proposal, we contribute to the development of the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5), by enhancing the ability of this ESM to integrate processes and feedbacks between the biosphere, atmosphere, cryosphere, land, and ocean. A practical limitation of ESMs, such as GEOS-5, is computational expense, which limits the possibility of comprehensively exploring component sensitivity to a variety of conditions. Furthermore, prescribing emissions of aerosol and trace gases, as is typically done, breaks down the fundamental point of running an Earth system model; that is, when these fluxes are prescribed, one cannot fully explore the coupled response of the Earth system to changing conditions. In this proposal, we address both of these limitations with respect to biomass burning, including its impact on the land surface and atmospheric chemistry, and the attendant feedbacks on other biogeochemical cycles in the Earth system.
We propose to quantify: 1) the response of fire activity to a warming climate and 2) the sensitivity of important components of the Earth system (e.g., oxidizing capacity of the troposphere, budgets of greenhouse gases (GHGs) and aerosols, and radiative forcing) to historic and potential future changes in fire activity. In order to explore the impacts of fire activity and its resulting feedbacks, we propose to (1) introduce and evaluate a coupled prognostic biomass burning emissions scheme in the NASA GEOS-5 Atmospheric General Circulation Model (AGCM), a capability that is currently lacking, (2) create a computationally-efficient tropospheric chemistry module ("Quick Chemistry") to explore the feedbacks between the major GHGs (ozone (O3), water vapor (H2Ov), halogens, N2O, CO2, and CH4), aerosols, and the Earth system, (3) perform historical sensitivity simulations to quantify the impact of fire activity on important components of the Earth system, and (4) perform simulations of a future warming climate to quantify the potential impact on fire activity over the coming decades and the concomitant impact on aerosols and GHGs, and also explore the sensitivity of these important components of the Earth system to changes in fire activity.
Clouds are a critical part of weather and climate prediction. Uncertainties in cloud processes dominate the water cycle and the formation of precipitation and extreme weather events. Potential changes to clouds in response to climate forcing (cloud feedbacks) are also the largest uncertainty in our understanding of the future evolution of the climate system. We propose to improve the representations of clouds and cloud systems in Earth system models (ESMs) for both weather and climate scales. In particular, we will focus on the representation of cloud microphysics in both deep convective clouds critical for the water cycle, and lower shallow convective clouds that are important to climate sensitivity. The goal is a seamless treatment of cloud microphysics that can work for both weather and climate, and integrate with different turbulence schemes for different types of clouds. We will do this jointly in the NASA Goddard Earth Observation System (GEOS) and NCAR Community Earth System Model (CESM). The goal is a unified treatment of cloud microphysics that will work with non-hydrostatic models down to cloud permitting scales. We will bring to bear advanced evaluation tools and methods to characterize uncertainty and perform evaluation against NASA satellite and sub-orbital sets and we will deliver model code and evaluation tools to the community in two ESMs.
NASA has made large investments in satellite observations of atmospheric composition but their use in the GEOS Data Assimilation System (DAS) at GMAO is still very limited. This project will (1) deliver and evaluate a comprehensive state-of-science atmospheric chemistry module in the GEOS Earth System Model (ESM) and DAS to simulate coupled gas-aerosol chemistry from the surface to the stratopause, and (2) apply this module to assimilate current and near-future satellite observations of atmospheric composition in the GEOS DAS. It will continue the successful Harvard-GMAO collaboration to implement the chemical module from the GEOS-Chem chemical transport model (CTM) into the GEOS system. Through this activity the GEOS-Chem CTM has been converted into a grid-independent ESMF-compliant model, so that the exact same code is used by the stand-alone CTM and as a chemical module within the GEOS ESM. This ensures that the continual stream of innovation from the large worldwide GEOS-Chem user community is straightforwardly passed on to the GEOS ESM chemical module, which always remains up-to-date and referenced to the latest standard release of GEOS-Chem. The GEOS-Chem chemical module is now in place in the GEOS ESM and is being used for very-high-resolution simulations of tropospheric ozone chemistry and for assimilation of OMI and MLS satellite ozone data in the DAS. Our focus so far has been on tropospheric ozone chemistry, but GEOS-Chem has state-of-science capability for coupled gas-aerosol chemistry in the troposphere and stratosphere. Here we will exploit this capability in the GEOS ESM chemical module, and couple GEOS-Chem aerosol chemistry to aerosol microphysics and radiative transfer through the modal aerosol module (MAM) already in place in the GEOS ESM. GEOS-Chem will provide a capability for coupled gas-aerosol chemistry at GMAO and will help to revitalize the Global Model Initiative (GMI) capability for stratospheric-tropospheric chemistry. We will apply the GEOS-Chem module in the GEOS DAS to multi-species chemical data assimilation in the troposphere, exploiting data from present and near-future satellite measurements. Our specific objectives are as follows:
This project will continue the partnership between Harvard and GMAO in developing a state-of-science chemical data assimilation capability for the GEOS system. As such it will make a major contribution to the development of an Integrated Earth System Analysis (IESA) capability and to the core mission of GMAO.
The long-term and large-scale dynamics of ecosystems are in large part determined by the performance of individual plants in competition for light, water and nutrients. However, current Earth system models (ESMs) lack a mechanistic formulation for scaling from individual plants to ecosystems in their land components, limiting their ability to self-consistently integrate biophysical, biogeochemical, and biogeographical processes, or realistically predict transient changes in vegetation and feedbacks between the terrestrial carbon cycle and climate change at decadal to century scales.
The Ecosystem Demography (ED) model introduced a mathematical scheme to represent individual behavior through height-structured canopy heterogeneity and patch dynamics. The ED scheme, however, does not allow for analytical descriptions, and can produce patterns that are difficult to explain; in addition, the unrealistic prediction of the space partitioning of tree crowns results in under-prediction of photosynthesis. An alternative method is now available in the perfect plasticity approximation model (PPA), which simulates canopy vertical stratification by cohort height and crown sizes through an approximation of the stochastic gap creating and filling processes in forests. PPA offers more tractable analysis while still maintaining realism of ecosystems with measurable parameters, and produces more accurate forest structure than ED. In addition, a gap probability based canopy radiative transfer scheme, the Analytical Clumped Two-Stream (ACTS) model, now provides further realism and efficient computation of the vertical stratification of light in heterogeneous canopies.
We propose to implement the PPA in the Ent Terrestrial Biosphere Model (Ent TBM) to predict global vegetation dynamics. The Ent TBM is the land ecosystem component of the NASA Goddard Institute for Space Studies (GISS) ESM. It was designed with the ED approach to simulate canopy demography. But instead of the ED equations for community dynamics, we will implement the PPA within the ED-based cohort and patch structure. The ACTS scheme was developed for the Ent TBM, and can directly incorporate the crown statistics from PPA to address foliage clumping and light partitioning in vegetation layers.
In prior work, we have successfully predicted the distribution of evergreen and deciduous forests with the GFDL land model (LM3) in sites spanning from boreal to temperate zones by deriving unbreakable trade-offs between leaf mass per area, leaf lifespan, maintenance respiration, leaf nitrogen, and litter decomposition rate. Similarly here, we will design a set of trait continuum-based plant functional types to expand to global plant functional diversity. We will conduct simulation experiments to predict: 1) vegetation distributions along temperature and precipitation gradients at the continental scale, 2) terrestrial C storage and dynamics at decadal to century time scales, and 3) ecosystem responses to changes in climate and disturbances. We will particularly focus on the multiple equilibria of tree and grass cover in savanna ecosystems due to climate-vegetation-fire feedbacks. We will intercompare model simulations with MODIS and ICESat/GLAS observations of vegetation distributions and plant traits from the TRY database. Model parameters will be optimized using Bayesian approaches and model uncertainty will be estimated accordingly.
This proposal is relevant to NASA Earth Science goals, by advancing dynamic global vegetation models to uncover the competitive combinations of plant traits and essential diversity relative to the quantities of cover extent, LAI, phenology, and biomass. It will thus provide improved prediction of the carbon cycle and natural vegetation change in the future. It is also relevant to the current Modeling, Analysis, and Prediction program Research Themes of coupling processes and improving the predictability in ESMs across spatial and temporal scales.
Adequate representations of cumulus convection and its interaction with environment in Global Climate Models (GCMs) is a prerequisite for GCMs to accurately simulate the water and energy cycles in the climate system and their changes in the future. Cumulus clouds cannot be explicitly resolved in contemporary GCMs, and, therefore, need to be parameterized. Despite its importance, the development of cumulus parameterizations has been notably slow primarily due to our lack of understanding of cumulus convection. Considering that many GCM biases stem from imperfect parameterizations of cumulus convection, the process-oriented diagnostics that could provide direct clues about deficiency of parameterization schemes will help accelerating the GCM development.
The proposed research targets two specific processes whose representations in GCMs are heavily affected by convection and large-scale cloud schemes in GCMs – moisture-convection coupling and cloud-radiation feedbacks. A set of the process-oriented diagnostics for the target processes will be produced with multiple datasets that includes the newly available NASA observations (GPM) and reanalysis product (MERRA2). Uncertainty associated with the process-oriented diagnostics that will be produced under the proposed research will be quantified by comparing diagnostic results made with NASA datasets to those made with data from other sources. Perturbed-physics ensemble simulations will be conducted with the NASA GISS GCM by varying key parameters in the convection and large-scale cloud schemes. The simulation results will be analyzed with the process-oriented diagnostics to identify links between the GCM's parameterization schemes and the GCM representation of the targeted processes. The process-orient diagnostics produced under the proposed research will be used to evaluate CMIP6 simulations to pinpoint necessary model improvements in regarding the target processes. The production and application of the process-oriented diagnostics package will be performed by building upon the PI's extensive experience and expertise on the convective parameterization, process-oriented diagnostics development and evaluation of multi-model simulations.
The proposed work will contribute to improve understanding of the moisture-convection coupling and cloud-radiation feedback processes in the tropics. Regarding the tight association of moisture-convection coupling and cloud-radiation feedback processes with tropical convection, an enhanced understanding of these processes will help us better understand the nature of tropical convection. The anticipated results of the proposed project will thereby contribute to enhance the representation of tropical convection and its interaction with environmental moisture and radiation in GCMs. The process-oriented diagnostics developed under the proposed research will contributed to an enhanced representation of the target process in GCMs, which will likely be achieved by improved representations of tropical convection. Therefore, it is expected that the outcome of the proposed work will accelerate GCM development, especially the parameterizations of convection and large-scale clouds, which will reduce the uncertainty of predictions based on the GCMs.
Atmospheric blocking episodes are important contributors to high-impact extreme weather and are a prime target of sub-seasonal prediction. Intrinsic predictability of blocking and sources of prediction errors have not been well characterized, which represent an opportunity for improved medium range weather forecasts.
We propose a multi-pronged approach to better understand predictability and to improve prediction of atmospheric blocking events and the associated extreme weather events. Two suites of models based on the Goddard Earth Observing System Model (GEOS) and the Community Atmosphere Model (CAM) will be used. We will first evaluate the performance of the models against the MERRA2 and ERA interim reanalyses and additional satellite observations from the Global Precipitation Active Passive (SMAP), Global Precipitation Mission (GPM), and the Tropical Rainfall Measuring Mission (TRMM). We will evaluate the models' ability to simulate blocking and weather extremes climatology, to faithfully capture blocking dynamics at a process level, and, in the case of the GEOS-based models, the ability to forecast blocking events and the resulting weather extremes with the models' data assimilation system. We will then carry out intrinsic predictability studies using a perfect model approach, as well as cross-model prediction studies. The suites of models that we use have versions that differ only in their representations of the stratosphere, moist processes, or soil moisture, and therefore allow us to isolate the contribution of these model components on predictability and prediction errors. The effect of numerical resolution will also be explored. The use of both the GEOS- and CAM-based models will allow cross comparisons and assessments of the robustness of the results across models. Lastly, we will generalize our results to a broader range of models in the Subseasonal-to-Seasonal Prediction Project dataset. The proposed research will enhance both NASA modeling capability and the science return on NASA's investment in modeling and space-based observations in the critical area of predicting extreme weather.
We propose to leverage data assimilation capabilities built into the Ice Sheet System Model (ISSM, part of the ice component of GEOS-5), and to improve on them, in order to reconstruct the state of the Greenland Ice Sheet from 2003 (start of the ICESat altimetry record and the GRACE gravity record) to present-day.
We will rely on the altimetry record from ICESat-1, IceBridge and CryoSat-2, the gravity record from GRACE, and the velocity record record from several NASA/ESA/CSA/JAXA missions (ERS, RadarSat, Alos/Palsar, Sentinel-1) to invert for the state of the ice, including ice rigidity, surface mass balance and basal friction at the ice/bed interface. We will rely on the adjoint-based temporal inversion methodologies developed in ISSM. In addition, we will use the extensive GPS record collected starting 1994 to inform reconstructions of loading history that are compatible with initializations of models for the Greenland Ice Sheet.
Each sensor will be assessed in terms of its ability to invert for a specific model input, and combinations of sensors will be explored to understand how to reduce the uncertainty in spatio-temporal inversions. This will be the case for example for inversions of basal friction. This parameter is indeed loosely constrained, and a-prioris are difficult to quantify. We will explore how combining sensors and tightening a-prioris on all other model inputs (such as ice rigidity or surface mass balance) will enable better inversions of this hard to model/measure parameter.
Finally, we will use the spatio-temporal inversions of model inputs to assess trends and patterns in their evolution, towards better projecting the state of the Greenland Ice Sheet in the next 50 years, and in particular its mass balance.
In order to achieve such comprehensive objectives, we will develop and validate the adjoint capabilities of ISSM, the cost functions used in the inversions, and improve the scalability of the system to apply it to entire ice sheets. The extensive experience of the ISSM team in running forward models will be used to better constrain which parameters should be inverted for, and what a priori information to be used in the inversion for yielding the best inversions.
The expected significance of this effort will be to improve hindcast initializations of polar ice sheets models, and improve projections of the mass balance of Greenland, as well as quantification of its contribution to global mean sea level rise in a changing climate.
In recent years, the discovery from NASA satellite observations of an aerosol layer in the atmosphere near the tropopause region (˜13-18 km), spanning a large area from the Middle East to Eastern Asia (20N-45N, 0-100E) during the boreal summer monsoon, the Asian Tropopause Aerosol Layer (ATAL), has sparked much interests in research on the origin, composition of the ATAL, and its relationship to transport processes of atmospheric constituents in the upper troposphere and lower stratosphere (UTLS) and the variability of the Asian Monsoon Anticyclone (AMA). Contemporaneously, a large body of research has been conducted, generating various hypotheses, e.g. the Elevated Heat Pump (EHP) hypothesis and others, regarding the roles of absorbing aerosols (dust and BC) in enhancing monsoon extreme precipitation and deep convection in northern India and the Himalayas foothills region, in conjunction with a strengthening of the AMA during the boreal summer monsoon. However the possibly connections among tropospheric aerosol-monsoon interactions, extreme precipitation associated with deep convection, atmospheric constituent transport processes in the UTLS, and formation of the ATAL have not been explored.
The main objectives of the proposed research are to identify and provide better fundamental physical understanding of a) relationships between Asian monsoon variability and UTLS transport processes, leading to the formation, and variability of the ATAL, and b) how these relationships may be affected under warmer sea surface temperature (SST) conditions. In this study, we propose to examine the role pre-monsoon build up of absorbing aerosol (dust and BC) on UTLS aerosol-chemistry transport processes during the peak monsoon season, focusing on the feedback processes (EHP and others) induced by absorbing aerosols (dust, BC and OC) and occurrences of deep penetrative convection events over northeastern India and Himalaya foothills region, and their impacts on transports of water vapor and aerosol species (BC, sulfate) and chemical gases (mainly CO) to the UTLS region and the ATAL. Interactive emission and dynamical feedback processes associated with winds, temperature, water vapor absorbing aerosol, and CO emissions as a function of climate variability and change will also be investigated. We will use AIRS, MODIS, Calipso/CloudSat, MLS, and MERRA2 data for exploration of relationship among aerosols, rainfall, temperature, winds, deep convection, tropopause height, and UTLS transport processes during the Asian summer monsoon on: a) seasonal to intraseasonal time scales, and b) interannual and longer term relationships with sea surface temperature variations. Numerical experiments will be carried out with the GEOS5 model in control and restrained-physics integrations to identify the roles of aerosol-monsoon interactions under prescribed present-day SST, and CMIP5 model projected global warming SST. Model results will be validated against the aforementioned satellite observations and MERRA2 reanalysis.
The proposed research is relevant primarily to the MAP research theme under Constituents in the Climate System, and secondarily to two other themes, Clouds in the Climate Models and Coupling in the Earth System.
In contrast to rapid Arctic sea ice loss, Antarctic sea ice extent (SIE) has shown a small but statistically significant increase since the late 1970s. The expansion of Antarctic SIE in a warming climate has puzzled the science community. Simulation of the observed Antarctic sea ice change remains a major climate model deficiency. We propose to advance the understanding of Antarctic sea ice change and to improve the representation of Antarctic sea ice in the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) using coupled replay simulations and observations.
The major difference between the proposed work and previous modeling studies is that we will use the GEOS-5 replay capability. By constrain the atmosphere to NASA Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2), the coupled replay simulations will enable us to separate the influences of atmospheric and oceanic processes on Antarctic sea ice multidecadal variability. We will quantify the impact of biases in atmospheric surface forcing on Antarctic sea ice trends in GEOS-5 by comparing free-running and replay simulations. We will isolate the effects of ocean eddy representation and surface freshwater forcing on Antarctic sea ice change and to quantify the atmospheric modulation on these effects. We will quantify and identify the mechanisms that control model's internal multidecadal variability of Antarctic sea ice. We will conduct sensitivity simulations to identify the feedback of Antarctic sea ice change on the atmosphere.
The proposed work will reduce uncertainties in Antarctic sea ice simulation and guide the improvements of Southern Hemisphere atmosphere and Southern Ocean processes in GEOS-5 and thus directly related to the MAP programmatic priority. Our research will address the MAP research theme of coupling in the Earth system by improving the understanding of the couplings between Antarctic atmosphere, Southern Ocean, and sea ice.
Nitrous oxide (N2O) is the third most important anthropogenic greenhouse gas (GHG) in the atmosphere and a major ozone depleting substance (ODS) in the stratosphere. Its atmospheric concentration has been increasing as a result of human activities, altering the natural nitrogen cycle. Total emissions have increased by ˜60% above preindustrial conditions, as deduced from the observed global mean concentration trend and its atmospheric lifetime. Although major N2O sources are known, the magnitudes of individual sources are highly uncertain. Accurate N2O emissions estimates from these sources and their past changes and future evolution have important implications for climate and for formulation of emissions mitigation strategies.
We propose to MAP a 4-year project with the NASA GEOS-5 model to simulate atmospheric and oceanic N2O and its isotopologues. Modeling and analysis of the N2O stable isotopologues has emerged as a promising means for constraining the budget, since N2O from various sources and sinks has distinct isotopic signatures.
The proposed main tasks are:
Upon successful completion of the proposed work, we will deliver:
This proposed investigation will greatly improve the global model representation of N2O, a major GHG and ODS, and its interaction with climate in the NASA GEOS-5 model. The proposed new N2O configuration will be a critical first step in future development of the nitrogen cycle in chemically coupled atmosphere-ocean-land within the Earth System Modeling framework. The proposed investigation is directly relevant to the research elements described in ROSES 2016 A.13, Modeling, Analysis and Prediction: "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" and "the development and implementation of physically-based interactive emissions parameterizations which can respond to climate change and other sources of variability in the Earth system".
Cloud scavenging is the dominant removal process for a whole suite of aerosols but model parameterizations of this process are highly uncertain, substantially contributing to large uncertainties in the simulated loadings and radiative forcing of aerosols. IPCC AR5 has identified the improvement of wet deposition and scavenging parameterizations in large-scale models as a priority. The current wet scavenging parameterization in the NASA GEOS-5 model is not explicitly linked to cloud droplet and ice crystal nucleation. Recent development of two-moment cloud microphysics in GEOS-5 allows treating cloud scavenging of aerosols more physically with self-consistency. We propose a 4-year project to develop more physically-based parameterizations of wet scavenging, and study the impact of their uncertainties on aerosol direct and indirect effects in GEOS-5. Our research objectives are:
This project will result in more physically-based parameterizations of aerosol scavenging by rain and snow in GEOS-5, and will improve confidence in our estimates of aerosol burden, lifetime, and direct and indirect effects. This proposal directly responds to the research theme "Constituents in the Climate System" in the solicitation by improving our understanding of the process of cloud scavenging of aerosols and its representation in the NASA GEOS-5 model. This research addresses NASA's Strategic Goal 3A1 to "Understand and improve predictive capability for changes in the ozone layer, climate forcing, and air quality associated with changes in atmospheric composition."
Convective mass flux lies at the heart of the cumulus parameterization of the global climate models (GCMs). Yet, no global observation of this critical parameter is available at this time. To correct the situation, PI and his collaborator proposed a novel, satellite-based approach to estimate convective mass flux, as well as the large-scale [O(100km)] total mass flux. The method uses a suite of synergistic satellite observations (TRMM, CloudSat, CALIPSO, MODIS, AIRS, and QuickScat) to constrain, in a Bayesian manner, the plume model solutions driven by AIRS/AMSU sounding. Initial results, as reported in two recent publications, are promising. The proposed work builds upon these activities and seeks to achieve the following goals:
The proposed studies are well aligned with the NASA Modeling, Analysis and Prediction (MAP) themes. Assessing satellite-based estimates of convective mass flux using GCE and ground-based radar data prepares an important diagnostic tool for studying "clouds in Earth System Models" Use of the estimated convective mass fluxes to evaluate GCM simulations of tropical convection and to improve cumulus parameterizations represents an "advanced method for model evaluation", which is explicitly called for in the solicitation, and addresses key "cloud processes and their representation in ESM".
Water, energy, and nutrients are the primary determinants of productivity in terrestrial ecosystems. In a changing climate, dynamic monitoring and local adaptation of management practices to changing resource levels are crucial to socio-ecosystem sustainability. A better characterization of terrestrial water, energy, and carbon cycles through the integration of observations into models at spatial and temporal scales conducive to decision making and adaptation responses are essential. Recent advances in remote sensing, terrestrial carbon and phenology modeling, and data assimilation techniques provide the tools we need to make significant progress towards enhancing our understanding of terrestrial cycles and phenology dynamics.
With this project we propose to implement an innovative terrestrial phenology data assimilation technique to integrate carbon-cycle observations into an ensemble modeling framework that will produce terrestrial carbon-water-energy reanalyses over the North American Land Data Assimilation System (NLDAS) domain at 1/8° and hourly spatial and temporal resolution. Specifically, we will evaluate the potential of assimilating phenology observations in land data assimilation system by constraining the modeled terrestrial carbon dynamics with remotely sensed observations of vegetation condition, water and energy availability (e.g., albedo, leaf area index, fraction of photosynthetically active radiation, and gross primary production). We will also assess the efficiency of a multi-model ensemble assimilation technique by including four land surface models that have a dynamic vegetation phenology component within the assimilation system. The multi-model ensemble will allow incorporating the uncertainty in the physical processes, which is still an unresolved assimilation issue. Furthermore, we propose to use remotely sensed water, energy, and vegetation observations to dynamically minimize systematic biases, using automatic parameter calibration procedures, and state initialization errors, using an ensemble land data assimilation system.
Besides answering outstanding science questions, this work will generate terrestrial carbon reanalyses from 1980 to present to be used in weather and climate forecast models and will provide sensitivity and uncertainty analyses to understand the limitations and constraints of the proposed approach. The Land Information System (LIS) will be used to integrate remotely sensed phenology observations, ensemble modeling, uncertainty/sensitivity analysis, and data assimilation capabilities to quantify the contemporary terrestrial carbon budget and associated uncertainties and their contributions from different sources.
We propose to implement a physically based model of the wind speed threshold for dust emission, while better constraining the regional mass fraction of iron oxides (a ubiquitous trace mineral within dust particles) in the NASA Goddard Institute for Space Studies ModelE. This will reduce the uncertainty of direct radiative forcing by dust and its impact upon climate, while providing a more confident estimate of the anthropogenic fraction of dust that is predominately the result of cultivation.
The wind speed threshold is a major uncertainty in the calculation of dust emission, particularly for anthropogenic sources. Current global dust models typically assume that the wind speed threshold is regionally invariant (except for variations related to soil moisture that are small within arid regions). This assumption results from the environmental similarity of natural sources of dust that often correspond to dry, ephemeral lakes. However, cultivated dust sources that result from soil disturbance are often located within environments that are distinct from those of natural sources. As such, the assumption of a uniform wind speed threshold for both source types is problematic. Our new physically based model of the emission threshold will replace current ad hoc prescriptions and lead to more confident estimates of the cultivated fraction of dust and its change since the pre-industrial. This model will be thoroughly evaluated and constrained using observations like the frequency of dust occurrence derived from MODIS Deep Blue retrievals.
In addition to determining the aerosol mass that depends upon the emission threshold, we will calculate dust radiative forcing and its climate effect that depend upon the dust composition. Earth system models typically assume a globally uniform mineral composition for dust particles, despite well-known variations in the mineral composition of source regions. We propose to use measurements by a suite of A-Train instruments along with AERONET to provide a stronger constraint upon the iron oxide fraction calculated by our new prognostic model of aerosol mineral content within ModelE. Dust radiative forcing is especially sensitive to this trace mineral that dominates shortwave absorption. We propose to implement and evaluate a minimal representation of the prognostic mineral model consisting only of iron oxides and an amalgam of the remaining minerals for users who need radiative forcing without the full computational cost of the entire suite of minerals.
The proposed improvements to ModelE will result in more confident regional estimates of dust and its climate impact. We are especially interested in precipitation anomalies in regions with extensive anthropogenic dust sources (like the Asian monsoon region) or limited food security (like the Sahel). The improved functionality of ModelE and its prediction of aerosol mineral content has applications to heterogeneous chemistry and dust nucleation of cloud droplets, along with ice nucleation of mixed-phase clouds. Dust also impacts human health.
Understanding the effect of anthropogenic dust is crucial for attributing twentieth-century climate trends. The proposed model development and evaluation responds to the theme of "Constituents in the Climate System", but we will additionally examine the climate response that results from the relation between local dust emission and the planetary-scale distribution of rainfall, addressing the theme of "Coupling in the Earth System".
The overarching objective of this project is the development of enhancements to the RRTMGP radiation code to support key GMAO and GEOS-5 priorities. RRTMGP, which is being developed under current support from the NASA MAP program, is a high-performance radiation code designed for the current generation of computational architectures. The code is a completely restructured and modern version of the RRTMG radiation code used in many GCMs. RRTMGP preserves the strengths of the existing RRTMG parameterization, especially the high accuracy of the k-distribution treatment of absorption by gases, but the entire code has been rewritten to provide highly efficient computation across a range of architectures. RRTMGP is planned to be the next radiation code in GEOS-5, and its initial version will be accurate for the typical GCM configurations of spatial and temporal resolution, vertical range, gas abundances, etc.
The enhancements to RRTMGP proposed here will extend the range of validity of GEOS-5 to support important scientific goals of GMAO and NASA-GSFC researchers. In particular, we propose to improve RRTMGP to support GEOS-5 Nature Runs at high spatial resolution, a critical priority for GMAO, both by including the impact of topography on radiative fluxes and by implementing strategies to speed up the code. We will also enhance RRTMGP to allow solar variability to be simulated more accurately in GEOS-5, to include the radiative effects of key hydrofluorocarbons, to accurately compute the radiation from massive emissions of sulfur dioxide from large volcanic eruptions, and to extend the upper vertical limit of the code above 65 km. RRTMGP also includes a satellite simulation capability, and we propose to add CrIS, AIRS, IASI, and OCO-2 to the suite of implemented sensors.
This development will significantly enhance the GEOS-5 representation of radiative processes, of critical importance across a wide range of spatial and temporal scales. This work would thereby support the MAP program objective of attaining a complete understanding of the Earth system, as well as enhance the quality of GEOS-5 simulations and reanalysis products produced by the model. These benefits would apply across the weather-to-climate continuum of temporal scales and extend to MAP projects related to GEOS-5, such as the Global Modeling Initiative and the NASA Unified Weather Research Forecast Model. This proposal will improve a component of NASA MAP supported models, a programmatic priority identified in this solicitation, and it is relevant to the research themes in the solicitation of assimilation, coupling in the Earth system, constituents in the climate system, and predictability in the Earth system.
Terrestrial water, ecosystems, and climate are intimately coupled, dynamic components of the Earth's system. In response to elevated CO2 and the resulting warming, terrestrial water and ecosystems have been experiencing substantial changes, e.g., globally shrinking snowpack, greening plants, earlier blooming and leafing out, and depleting groundwater. The "memory" of climate change retained in the slowly varying states of snow, soil water, and groundwater can persist variably from seasons to years, feeding back to climate variability with varying energy and water fluxes mainly via plant roots and stomata. Based on the widely used Noah land surface model (LSM), Noah-MP systematically improves the realism of snow, soil water, groundwater, and vegetation schemes and enhances the terrestrial water and ecosystem dynamics. However, whether the enhanced dynamics and improved realism of terrestrial water and ecosystems help improve climate predictions has not been investigated. Like many other LSMs, Noah-MP uses static, shallow representations of roots and neglects root dynamics, resulting in low ecosystem resilience to drought and a shorter climate "memory."
We propose to
We will test three major hypotheses:
H1: With more realistic schemes of snow, groundwater, and leaf and root dynamics, Noah-MP/NU-WRF will produce more realistic terrestrial water and ecosystem states as well as more realistic climatology and climate variability than Noah/NU-WRF.
H2: Including leaf and root dynamics that can adapt to climate change will enhance terrestrial ecosystem resilience and transpiration and produce more realistic climate variability.
H3: Shrinking snow cover has accelerated warming over the CONUS since the 1980s through positive snow-albedo feedback; less (more) snow accumulation in winter may result in less (more) snowmelt water infiltrating the soil in spring and hence less (more) vigorous vegetation and a consequent effect on summer temperature and precipitation.
This proposal is a direct reply to the MAP theme of "Coupling in the Earth System" for "developing an understanding of the Earth as a complete, dynamic system." The proposed study will help "lead to an improved understanding and representation of the interactions between different Earth system components - such as land-atmosphere, … , or the interaction of the cryosphere with other components."
The Great Lakes Basin (GLB) has been a regional hotspot of climate change impacts, including lake warming, declining ice cover, and increased lake-effect snowfall. Modeling studies have demonstrated the substantial contribution of lake-atmosphere interactions and uncertainty in lake-effect snowfall projections. Despite the vast socio-economic impacts of lake-effect snowstorms (LES), these extreme events have received minimal attention from IPCC and National Climate Assessment. The insufficient investigation of projected changes in these cold season extremes is partly due to the general lack of appropriate modeling tools that properly represent the Great Lakes and lake-atmosphere interactions. There have been recent advances in the representation of lake-atmosphere interactions among regional climate models through 2-way coupling with 1D lake models, permitting the simulation of broad-scale lake-effect snow features. However, such lake models are inappropriate for deep lakes, leading to large biases in lake surface temperature (LST), ice cover, and evaporation. Xue et al. employed offline and 2-way coupling of the 3D Finite Volume Community Ocean Model (FVCOM) to WRF and RegCM4 and demonstrated the benefits of incorporating 3D models to represent Great Lakes' hydrodynamics, including dramatic bias reductions and accurate turbulent flux representation, with implications for LES. Recent advances in data collection, including over-lake flux measurements through the Great Lakes Evaporation Network (GLEN) and Ontario Winter Lake-Effect Systems field campaign, along with improved spaceborne cold season cloud/precipitation detection by CloudSat and Global Precipitation Measurement (GPM) platforms, have deepened the understanding of lake-atmosphere interactions, LES dynamics, and shallow cumuliform snow microphysics.
We proposed to evaluate and advance the representation of lake-atmosphere interactions and heavy LES in the GLB by NU-WRF. NU-WRF permits 2 crude lake treatments: remotely-sensed LSTs are provided as boundary conditions or the atmosphere is 2-way coupled to the 1D FLake model. In addition to exploring LSTs, turbulent fluxes, clouds, and LES in these configurations, we will enhance the representation of 3D lake processes and lake-atmosphere interconnections by developing a 2-way coupled lake-atmosphere-land model, NU-WRF/FVCOM, which permits the development of dynamically downscaled LES projections. Historical NU-WRF runs will be produced for the aforementioned lake treatments and evaluated against observations to quantify added value to over-lake turbulent fluxes and LES frequency/intensity from incorporating 3D lake processes and advanced lake-atmosphere coupling. GLEN measurements will be used to evaluate turbulent flux responses to ice cover and temporal distribution of evaporation events. For a set of observed LES, we will create a NU-WRF/FVCOM ensemble, exploring the impact of microphysical, boundary layer, convective, and radiation schemes; vertical resolution; and grid spacing on morphologies of lake-induced mesoscale circulations. By varying parameterizations and performing runs with imposed lake temperature anomalies, the sensitivity of spatio-temporal patterns of lake-effect snowfall and resulting snowfall biases can be attributed to LST or microphysics biases. Evaluating the representation of heavy LES in NU-WRF will be grounded in CloudSat/GPM products. We will evaluate CloudSat/GPM snowfall retrievals against station observations. In addition to evaluating NU-WRF against CloudSat/GPM vertical radar reflectivity transects for case studies, we will examine the spectrum of snow events through density functions of radar reflectivity-based estimates of snowfall intensity/frequency and cloud attributes, allowing for the evaluation of the model’s microphysics. Radar simulators will be applied to model output to compare radar reflectivity distributions and characteristics between simulated and observed LES datasets.
Observations indicate that the Greenland and Antarctic ice sheet have contributed approximately 7.5 mm and 4 mm respectively to global sea level rise over the 1992-2011 period, and that their contribution to sea level rise is accelerating. Sea level rise has been identified as a long lasting consequence of anthropogenic climate change, as sea levels will continue to rise even if temperatures stabilize. Risk assessment and adaptation efforts depend on an assessment of future rates of mass loss from the ice sheets.
In addition to their impact on sea level rise, ice sheets influence the Earth's climate across different spatial and temporal scales due to changes in freshwater fluxes, orography, surface albedo and vegetation cover: Ice sheet evolution and iceberg discharge affect ocean freshwater fluxes, which can affect oceanic circulation. Changes in ice sheet orography modify near surface temperatures by altering atmospheric circulation on both regional and global scale. Changes in surface albedo due to ice sheet migration have played an important role in past interglacial-glacial transitions. Thus while ice sheets are recognized as important players in the Earth climate system quantification of feedbacks between ice sheet and climate is still lacking.
We propose continued development of the coupling effort of ice sheet models to the two NASA climate models (GEOS-5 and GISS ModelE) to gain insight into the effects of dynamic ice sheets and associated feedbacks on the global climate system. We will use the existing two-way ice sheet-atmosphere coupling developed in the first phase of this effort and combine it with the latest insights into ice sheet-ocean interactions. Specifically, we propose to improve the current state of the art parameterizations of melt rates beneath floating ice shelf and generalize these parameterizations so that they can be used as a first order coupling between ice sheet and global coarse ocean models, using insight from existing high resolution coupled ocean-ice sheet model (MITgcm coupled to ISSM).
Our science will explore the following questions: Can we successfully hindcast the mass loss rates seen in GRACE solutions for Greenland and WAIS? Are current rates of freshwater input currently influencing the North Atlantic circulation? Do improved estimates of Antarctic freshwater flux help explain the relatively stable Antarctic sea ice extent? Can we provide credible improved estimates of future mass loss?
Recent observational analyses (Naud et al. 2015; Crespo and Posselt 2016) indicate that changes in the large scale environment are associated with systematic changes in cloud vertical structure in frontal regions of extratropical cyclones. These changes appear to depend on the stage of cyclone life cycle, as well as the ocean basin. There is evidence to suggest that the incidence of deep convection along fronts increases in a warming environment, and a recent case study analysis indicates that there may even be stratiform to convective transitions within fronts during the evolution of a single storm. Changes in the characteristics and coverage of convective cloudiness have implications for the evolution of precipitation in the middle latitudes in a warming climate. There is currently a lack of consensus among climate model predictions as to how ETC strength may change in the future, and recent model sensitivity studies indicate it is likely that uncertainty in treatment of frontal scale moist processes and their interaction with storm dynamics is a key contributor.
NASA's Global Modeling and Assimilation Office (GMAO) has recently released the second version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). There have also been recent and planned substantive changes to the Global Environmental Observing System (GEOS) model physics packages. The availability of a robust and well tested front identification algorithm along with a NASA-produced algorithm for objectively identifying extratropical cyclone centers and storm tracks, and a large array of NASA satellite observations afford the opportunity to simultaneously examine the incidence of stratiform and convective cloudiness in frontal zones, as well as evaluate the representation of synoptic and mesoscale moist processes in MERRA-2, and GEOS.
The research described in this proposal will apply proven techniques for analysis of moist processes in frontal extratropical cyclones to the analysis of moist processes in NASA models and reanalyses. It draws upon a pre-existing multi-year database of A-Train transects of mid-latitude fronts, and extends this database to examination of convective and stratiform cloudiness in frontal regions. Partnership with model developers at NASA GSFC will allow use of the observations in model evaluation and in model physics sensitivity studies, which will result in better characterization of model error and advances in model development. We will focus specifically on representation of moist processes in the new MERRA-2 reanalysis and on the realism of simulated convective and large scale precipitation from the GEOS model. A key question we will address in analyzing the GEOS simulations is the effect of decreasing grid cell size (increasing resolution) on the realism of frontal clouds and precipitation.
References
Crespo, J.A., and D.J. Posselt, 2016: A-Train Based Case Study of Stratiform - Convective Transition within a Warm Conveyor Belt, Mon. Wea. Rev., 144, 2069–2084.
Naud, C.M., D.J. Posselt, and S.C. van den Heever, 2015: A CloudSat-CALIPSO View of Cloud and Precipitation Properties Across Cold Fronts Over the Global Oceans. J. Climate, 28, 6743-6762.
We propose to enhance the GEOS-5 subseasonal to seasonal (S2S) forecast capabilities by integrating ocean biogeochemistry into the current forecasting system. Using the NASA Ocean Biogeochemical Model (NOBM), already implemented into GMAO's GEOS-5 Earth System model, we will develop a forecast system for global ocean biology. This new development can improve our understanding of the seasonal and interannual changes in the biology, recruitment of higher trophic levels, and improve ocean heat content representation in the oceans. This proposal directly contributes to improving the "Predictability in the Earth System" area called in the MAP solicitation. The GEOS-5 and NOBM will be forced using the seasonal forecast of atmospheric and ocean conditions developed as part of the GMAO core work. The forecast skills will be evaluated using retrospective forecasts and comparing these to satellite ocean color data. These retrospective forecasts will be used to quantify the uncertainties in the forecast of ocean biogeochemistry at lead times from weeks to 9-months. A focus of this study will be in assessing the skills of this forecast in predicting the occurrence of El Niño Southern Oscillation events on phytoplankton concentration in the Pacific Ocean as well as the physical and nutrient conditions leading to these events. This effort can potentially assist in understanding and forecasting biological conditions related to commercial fisheries, improve forecasting of temperature and heat content in the oceans, and help guide new NASA field programs.
Lightning measurements provide a strong proxy for in situ microphysical and kinematic conditions found in a wide spectrum of convection, including extreme thunderstorms. Importantly, to better detect, improve process level understanding of, and subsequently predict the occurrence of these extreme storms, information on storm physical characteristics such as cloud and precipitation water content, and vertical velocity are critical; lightning is strongly related to these storm properties. Vertical velocity measurements and tendency are often very difficult to acquire in real time, but lightning serves as an indicator of rapid, intense updraft growth. Rapid, intense updraft growth highlights the potential for severe weather (e.g., hail, high winds, tornadoes) and also is related to the occurrence of heavy rainfall.
Previous work shows that inclusion of lightning electrification schemes into modeling has improved the model's microphysical representation of thunderstorms. The most important aspect to an electrification scheme is the representation of ice microphysics within the model. Ice crystals and graupel particles in the presence of supercooled liquid water are the foundation for electrification in thunderstorms. Concentrations and size of these hydrometeors directly relate to the charging potential within the cloud and these properties are physically controlled by kinematic features in the storm like cloud updraft speed and diameter.
NASA's Unified Weather Research and Forecasting model (NU-WRF) is the ideal candidate for implementation of an electrification scheme due to its well-developed and validated suite of microphysics schemes including the most recent Goddard-4 ice microphysics scheme. Development NU-WRF has focused on the incorporation and improvement of various NASA-led schemes for physical parameterizations, microphysical processes, and the use of NASA satellite products in evaluating model performance. The NU-WRF also includes a satellite simulator, which provides a way to validate that the model accurately captures the increase/decrease of lightning in line with observed events (using GOES-R temporal frequency) or that statistical views of lightning properties from NASA developed lightning instruments [e.g., Tropical Rainfall Measurement Mission (TRMM), International Space Station Lightning Imaging Sensor (ISS-LIS) and the Geostationary Lightning Mapper (GLM)] could be used to compare to the scheme's performance.
Recent interaction with Laboratoire d'Aérologie at the University of Toulouse provides the ideal opportunity to incorporate an electrification scheme relevant to NASA modeling capabilities and lightning observations. The scheme is called the Cloud Electrification and Lightning Scheme (CELLS). CELLS is the optimal electrification parameterization because it simulates optical energy from the top of the cloud from lightning, a measurement which the ISS-LIS and GLM will directly measure. Comparison between NU WRF model output with space and aircraft-borne lightning instrumentation will used to validate and improve the CELLS electrification and Goddard-4 ice microphysics schemes within the NU-WRF framework for improved representation of intense storms within the model framework. Upon successful implementation and validation of the CELLS scheme into NU-WRF, the new model configuration will be used to improve our understanding of the structures in extreme storms to foster accurate predictions of events that threaten life and property. The launch of ISS-LIS and GLM by the end of 2016 will usher in a new era of remote sensing of these extreme weather events which directly impact society. A complementary modeling component will enhance NASA’s ability to understand basic science questions related to thunderstorm electrification in extreme storms, as well as, applications to NASA missions [e.g., Global Precipitation Mission (GPM) and Tropospheric Emissions: Monitoring of Pollution (TEMPO)].
In a series of seminal papers NASA funded research put forth the "Brown Ocean" theory. The Brown Ocean theory is based on the "Green Ocean" analogy that has been put forth to describe the contributions of the Amazon Forest to the regional hydroclimate regime. In the Brown Ocean, UGA studies defined the new terminology to describe tropical cyclones that maintain or increase strength after landfall. In possibly the first global assessment of tropical cyclone maintenance/intensification (TCMI) events, these studies evaluated post-landfall structure and strength of inland tropical cyclones in the major basins. The study evaluated storms from 1979 to 2008 using NOAA IBTRACs data. NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA) data were used to diagnose environmental conditions and atmospheric structure. Out of 227 cases, 45 increased or maintained strength, as determined by wind speed or pressure. The "hotspot" for TCMI events was Australia, but cases were also found in the United States and China. The analysis suggested that "Brown Ocean" environment consists of three observable conditions: (1) a barotropic lower atmosphere with minimal temperature variations, (2) sufficient antecedent soil moisture, and (3) latent heat flux values from evaporation that reach at least 70 watts averaged per square meter. Because inland rainfall and wind impacts from tropical cyclones are significant, more rigorous evaluation is required. NASA's observational and modeling assets, including the Soil Moisture Active and Passive (SMAP) mission and the NASA Unified WRF (NUWRF) and coupled Land Information System (LIS) models, are ideal for confirming the Brown Ocean concept with a more rigorous scientific treatment. Specific research questions include: 1) How do natural variability and heterogeneity of land surface moisture (i.e. significant wet/dry anomalies driven by precipitation, impacts of inland water bodies such as lakes) contribute to the intensification of an inland TC? 2) Do human- managed landscapes such as irrigated areas contribute to the intensification of an inland TC through the availability of seasonal land surface moisture? 3) To what degree does satellite-based soil moisture assimilation (e.g. SMAP) improve TC intensification prediction?
We will develop a cyclone-basin specific composite soil moisture climatologies for the period of record of SMAP and compare that with longer-term climatologies generated by MERRA-2. We are particularly interested in identifying relative hotspots during the SMAP period of record relative to a 30-Year MERRA-2 Normal. The 36-km SMAP radiometer –based products will primarily by utilized in this analysis.. We will also collaborate with NASA to conduct a series of land data assimilation (DA) experiments using the NASA Unified WRF (NU-WRF) model, coupled to the Land Information System (LIS). This work will strongly leverage current NASA field, modeling and observational activities. We are directly addressing the NASA request for the Modeling, Analysis, and Prediction (MAP) themes of (RFP section 3) a) Extremes in the Earth System (hurricane intensity and structure), b) Coupling in the Earth System (land-atmosphere interactions), and c) Assimilation (ability to assimilate short-term records of soil moisture).
This proposal is focused on advancing weather and climate assessments and forecasting by improving (1) high frequency, global satellite observations of clouds, aerosols, and surface properties derived from operational geostationary-orbit (GEO) and NASA low-Earth-orbit (LEO) satellites, and (2) their use in evaluation and near real-time (NRT) assimilation to improve the Global Modeling and Assimilation Office (GMAO) GEOS-5 assimilation system. Building on the collaboration fostered by the MAP program between the NASA Langley Research Center (LaRC) satellite remote sensing team and the GMAO, we propose to continue to refine, expand, and improve the research accomplished under our previous MAP awards. Specifically, we will accommodate and capitalize on data from the new generation of advanced GEO imagers, including GOES-R, and also from LEO satellites to further improve the observations, model analyses and forecasts over a wider range of conditions at all latitudes. Under our previous MAP awards, we constructed the Satellite ClOud and Radiation Property retrieval System (SatCORPS) and employed it to analyze in NRT, hourly 8-km data from 5 GEO imagers providing a wide range of cloud and radiation property retrieval products to the GMAO and other researchers. We developed a novel cloud data assimilation (CDA) system utilizing satellite cloud optical depth (COD), temperature, and altitude estimates which has led to improved cloud analyses, and was found to profoundly impact the land surface energy budget, heat and moisture forecasts. We developed a new approach to expand the range and accuracy of nocturnal satellite COD estimates to improve cloud characterizations and impacts over the full diurnal cycle. We also developed a satellite skin temperature retrieval, which has allowed major advances in understanding the GEOS-5 regional and diurnal land surface temperature behavior. We also developed a method to estimate aerosol optical thickness (AOT) over ocean from clear GEO visible reflectance. While much has already been accomplished and learned, there is still enormous potential to better inform cloud, aerosol, and surface processes resolved in GEOS-5 and to further improve significant aspects of GEOS-5 output with satellite-derived guidance. In this proposal, we will continue to develop, validate, and improve satellite cloud, aerosol and surface parameters over a wider range of conditions. Active remote sensor data from satellite and surface sites will be used to further improve daytime and nocturnal passive satellite cloud optical and microphysical properties, and cloud vertical structure. We will continue to improve skin temperature estimates by better accounting for solar and view angle dependencies and to test core GEOS-5 land surface model updates. We will expand and improve the CDA system and produce a 3-year multiple GEO and LEO based global dataset of cloud properties and associated radiation outputs. We will then use the CDA-corrected surface radiation to force the land surface within GEOS-5 to improve the modeled surface moisture and energy states, and associated land-atmosphere fluxes. The CDA data and methods will also be used to examine, on yearly and global scales, the impact of sub-gridscale moisture variability on AOT and aerosol direct radiative forcing. Finally, to better constrain aerosols over the diurnal cycle and better account for rapidly changing conditions associated with high impact weather, we propose to adapt a Neural Net approach currently used in GEOS-5 to retrieve AOT from cloud-cleared MODIS reflectances to the high frequency GEO cloud-free observations from SatCORPS over land and ocean surfaces. Since higher resolution data is more effective in assimilation due to the variability at small scales, we will expand our NRT processing system to handle ˜4-km resolution data or better and complete the transition of this system to the NASA Center for Climate Simulation (NCCS) supercomputer facility.
We propose a project focused on studying tropical cyclones (TCs) in a new version of the NASA GISS climate model at 0.5 degree (50 km) horizontal grid spacing. Our project will have two components. In the first, we will experiment with physics and numeric parameters to develop a model that will be competitive for the study of tropical cyclones on the global scale. In the second, we will use the model for scientific studies of tropical cyclones and climate.
The highest resolution version of the GISS model to present has been one with 1 degree horizontal grid spacing. The TCs in this model are similar to those in other low-resolution models: they have low intensities, very large spatial scales, and persistent biases in their climatological genesis locations (particularly, too few storms in the North Atlantic and Eastern North Pacific). Research with other models has shown that a transition to much higher-fidelity simulations of TCs often occurs as horizontal resolution drops below 1 degree. TC simulation also depends critically on model physics and numerics, however. Experimentation coupled with careful model evaluation is needed to develop a model which simulates TCs well on the global scale, without degrading the quality of the broader climate simulation.
For model development, we will conduct experiments in which aspects of the model physics and numerics are varied, and evaluate the results to examine both TCs and the broader model climate. The physics testing will focus on the model convective parameterization, specifically parameters related to entrainment, rain re-evaporation, and cold pools. Our team has substantial experience with this scheme, having developed modifications in past projects which have now become part of the operational model due to our analysis of the improvements in the simulated Madden-Julian oscillation which resulted from them. Model numerics tests will include the role of divergence damping, a parameter shown to be important in TC simulation in the high-resolution global models at the NOAA Geophysical Fluid Dynamics Laboratory (GFDL), which are arguably the best in the world for TCs. As we vary the model we will carefully evaluate the model’s simulation of TC numbers, intensities, and patterns of genesis and tracks, as well as its mean climate and subseasonal variability, to determine the optimal configuration.
Studies of the influence of climate change on TCs will build on our prior experience from the US CLIVAR Hurricane Working Group and related projects, in which we have both contributed simulations with the 1 degree GISS model and diagnosed results from many other models. We will perform simulations for a range of warming scenarios, examining the roles of sea surface temperature change, direct greenhouse gas and aerosol forcings (using the atmospheric model coupled to a slab ocean mixed layer), and other influences. An area of particular interest, and one very little studied previously, will be the influence of climate change on TCs which make extratropical transition, such as Hurricane Sandy (2012). In addition to studying the influence of climate change on TCs, we will also study the influence of the El Nino-southern oscillation (ENSO) phenomenon.
Tropical cyclones are among the most destructive natural hazards. How they will change as the climate warms remains an important question with significant un-answered aspects. High-resolution models have been critically important tools in improving confidence in our understanding of the TC-climate relationship over the last decade. NASA GISS has been largely left out of this activity due to the lack of a sufficiently high-resolution GISS model. This is about to change, and our project will aid this transition. This proposal is relevant to the theme "Extremes in the Earth System" within the MAP Solicitation.
Overview and Proposed Research: This proposed modeling effort is a joint Global Modeling and Assimilation Office (GMAO) and Goddard Mesoscale Atmospheric Modeling Group project. It is aimed at improving our understanding of cloud and precipitation processes over many scales of motion, from the cloud microphysical scale to the large-scale circulations that organize the growth and decay of cloud and precipitation systems. 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. The main areas of proposed research are:
Relevance: Our proposed research meets the requirements and addresses the scientific problems as stated in the Modeling, Assimilation and Prediction (MAP) NRA under the category "Cloud and Earth System Models."
Our proposed research will also address the programmatic priorities as stated in the MAP16 NRA under the following research themes:
As a result of this proposed research, more realistic NASA model simulations of cloud and deep convective processes will be realized. In addition, this proposed research would provide for the distribution of simulated high-resolution cloud and precipitation data sets through a cloud library.
Ice sheets and glaciers play a crucial role on the Earth's climate system, affecting atmospheric circulation, freshwater fluxes and, crucially, sea level change. The surface energy and mass balance is a crucial mediator of these effects and improving the realism of these processes in model simulations has important consequences for model projections of future climates. Specifically, changes in albedo over time (seasonally and on climate time scales) play a key role in surface melt variability. The deposition of anthropogenic and natural aerosols can also directly modulate the surface albedo. Fresh snow accumulation increases albedo over permanently ice covered areas, while during summer, albedo decreases as a result of the melting.
In this project, we propose to investigate the relationships between albedo and surface mass balance over the Greenland and Antarctic ice sheets and other glaciated areas of the globe on atmospheric and surface mass balance. We examine natural seasonal variability and the forced variability of future projections using a new, physically consistent method for determining dynamic ice albedo scheme in the NASA GISS-E2 global climate model. We aim at addressing the following science questions: What is the relationship of albedo over the ice sheets to atmospheric fluxes? How well can the NASA GISS-E2 model reproduce extreme events, such as the high extreme melting year of 2012 (when melting extended up to ˜ 95 % of the ice sheet) and the low melting year of 1992 (due to the Mount Pinatubo eruption)? What is the impact of a dynamic albedo on surface mass balance and the role of natural and anthropogenic aerosols.
Up to recently, the broadband albedo for ice sheets in the NASA GISS-E2 model was static and set to the observed mean value of 0.8 for all months and regions. This limited the quality of the simulation of surface mass balance quantities (such as runoff, meltwater production, etc.) and of ice-atmosphere coupling. In this project, we will make use of a new albedo scheme for the NASA GISS-E2 model that has been developed and is being refined at NASA GISS. This will leverage current funding of postdoctoral researcher Dr. Alexander (NASA GISS) who is evaluating a regional model simulation of snow density's modulation of surface albedo, Dr. Fischer who is developing methods of coupling GISS-E2 and ice sheet models and PI Tedesco (LDEO, Columbia and NASA GISS) who is examining the effect of light-absorbing impurities on the snow/ice surfaces and the evolution of grain size associated with melting and refreezing cycles. We will also use aerosol deposition from the model to include the changing impact of light absorbing impurities in the new albedo scheme and the associated impact on grain size evolution. We will also make use of a sub-grid-scale scheme of elevation classes that allows for higher resolution simulation of ice sheet surface processes on elevation classes.
We will assess the albedo scheme through the comparison with satellite data (e.g., MODIS, AVHRR), in-situ measurements, re-analysis products (e.g., MERRA-2) and the outputs of a regional climate model (e.g., MAR). Model simulations will be studied in comparison with present-day remote sensing and in situ observations, and with the atmospheric outputs of regional climate models forced at the lateral boundaries with GISS-E2 outputs. We will use MODIS land surface temperature; melt extent and duration from passive and active microwave data; CALIPSO and CLOUDSAT data for cloud cover, vertical profile, radiative fluxes; CERES for radiative fluxes; AIRS for temperature and water vapor profiles.
Our project addresses the following research themes 'Coupling in the Earth System', 'Extremes in Earth systems' and 'Constituents in Climate system' and the following Programmatic Priorities: 'characterize and/or help reduce uncertainties in the models and products', 'extend the range of model or product validity by using new components'.
Objective: The key objective of this project is to implement into GEOS 5 and to evaluate a new fully unified boundary layer and deep convection parameterization based on the multiple-plume Eddy-Diffusivity/Mass-Flux (EDMF) parameterization. This is a turbulence and convection parameterization that can be considered as fully unified, since it is able to represent convective processes from boundary layer convection (dry and with clouds) to deep moist convection.
Technical Approach: Eddy-Diffusivity/Mass-Flux (EDMF): The EDMF approach is based on the unification of concepts generally used for the parameterization of turbulence in the boundary layer (ED) and of moist convection (MF). The EDMF approach was first proposed by Siebesma and Teixeira (Proc. Amer. Meteor. Soc., 2000) and implemented in the ECMWF model. Studies have shown the potential of EDMF to represent dry (Siebesma et al., JAS, 2007) and moist convective boundary layers (Soares et al., QJRMS, 2004).
In the last few years the PI's group has developed a new version of EDMF that is particularly well suited to simulate moist convective boundary layers and is able to represent in a realistic manner the dry boundary layer, stratocumulus, and shallow cumulus convection. This new version (Suselj et al., JAS, 2012, 2013) uses a multiple-plume approach where cloud base or surface variability of updraft properties is parameterized using a simple diagnostic variance equation. In the latest version, the Gaussian PDF of updraft properties in the surface layer is sampled in a Monte-Carlo manner to start a variety of updraft plumes. In addition, lateral entrainment is also stochastic using a distribution inspired by Romps and Kuang (JAS, 2011). Note that in this approach we assume that the area occupied by each one of the many plumes is fixed and remains constant in height as long as the plume is active – the total updraft area in a grid box is then simply the sum of the areas of the active plumes.
This new EDMF parameterization has now been extended to deep convection, allowing for a full unification of the parameterization of all turbulent and convective processes that occur in the Earth's atmosphere including deep moist convection. A necessary step to extend EDMF to precipitating deep convection conditions is to couple the MF component to a full microphysics scheme. In this new version of EDMF the plumes are coupled to a cloud microphysics scheme. The physics of downdrafts and cold pools associated with deep convection is also included.
This new fully unified EDMF parameterization is now able to take the horizontal resolution into account, which means that it is scale-adaptive. A necessary step to introduce 'scale-adaptivity' into the EDMF parameterization was to retain the updraft area term in the full EDMF decomposition of the subgrid vertical fluxes (e.g. Siebesma et al., JAS, 2007). Until this new version, EDMF versions (and most MF param-eterizations) basically made the assumption that the updraft area is always significantly smaller than the horizontal grid size and can be neglected in some terms. This is not the case any longer with the new EDMF parameterization. In addition, this new EDMF version offers an extra avenue to deal with the issue of making the parameterization scale-adaptive: the number of plumes can be made dependent on the grid size by taking into account the typical convective cell size versus grid size.
Changes in cloud structure and properties with changing atmospheric circulation have been considered a potential source of strong radiative feedbacks on climate change, and understanding them has been designated the first WCRP Grand Challenge. A recent investigation of reanalysis data and satellite observations found a clear and consistent relationship between the width of the Hadley cell and the distribution of high clouds, statistically significant in nearly all regions and seasons. In contrast, shifts of the midlatitude jet correlate significantly with high cloud shifts only in the North Atlantic region during the winter season. While in that region and season poleward high cloud shifts are associated with shortwave radiative warming, over the Southern Oceans during all seasons they are associated with shortwave (SW) radiative cooling. This analysis provides a significant observational constrain related to the interaction of clouds with atmospheric dynamics, that can be used to evaluate climate model representation of cloud processes.The analysis can be extended to cover the full spectrum of interactions between clouds and the major atmospheric circulation cells.
We propose to create a comprehensive evaluation methodology that will (a) use observational analysis to derive constraints related to dynamics/clouds/radiation interactions, (b) use model simulations to rank the importance of the derived constraints on climate sensitivity, (c) apply innovative techniques on model and observational data in order to understand the cloud and dynamics processes involved in the climatically important interactions, and (d) test and improve the boundary layer, cloud dynamics, and cloud microphysics schemes that are currently been implemented in the GISS ModelE. The proposed model evaluation methodology addresses a critical area of climate modeling research, namely the interactions between clouds, radiation, and atmospheric dynamics. It will use advance techniques to not only test the climatic importance of the derived observational constrains but to also understand the processes that are responsible for the model cloud deficiencies. The methodology will be used to test and improve ModelE cloud process representation, and will also be made available to climate modeling groups for evaluation and cloud simulation improvement purposes.
Droughts are among the most expensive recurring natural disasters to affect the United States (US). Because of their enormous socio-economical impact, society increasingly requires skillful and reliable prediction of these regional extremes in order to prepare for and mitigate their impacts. The model prediction of US drought, however, is often adversely impacted by distinct mean model biases. Notable biases, common across models, include distinct wet biases over the west and central Pacific and west tropical Atlantic that persist from spring to fall and peak in summer, and dry and warm biases over the central US during warm seasons. Recently, we have successfully removed much of the mean bias in the NASA GEOS-5 AGCM relative to the MERRA-2 reanalysis by applying 6-hourly climatological corrections (relative to MERRA-2) to model basic state variables within the free-running AGCM. Motivated by this recent development, this proposal, which builds upon our prior extensive work on US drought, will perform a comprehensive process-level investigation of the impact of model mean bias on the simulation and prediction of US drought.
Our proposed work has three thrusts. First, we will perform a process-level diagnosis to investigate the sources of the GEOS-5 model mean biases over the US. This includes determining the relative influences of model deficiencies in local and remote regions on the model mean biases over the US, and diagnosing the physical origin of these regional model deficiencies through a weather forecasting approach. Second, we will investigate the impact of the GEOS-5 model mean bias on the simulation of US regional drought and the model's representation of land-atmosphere coupling in the US. This includes investigating how the reduction of model mean biases improves the model’s representation of major sources of US drought predictability, including leading modes of subseasonal atmospheric circulation variability, leading patterns of SST variability on interannual to multi-decadal time scales, and the observed long-term global warming trend. Third, we will assess the ability of the GEOS-5 model to forecast US regional drought, and investigate the impact of mean model bias on drought forecast skill. This includes investigating whether the positive impact of realistic land initialization on forecast skill increases in a bias-corrected environment.
The proposed work targets four priority areas solicited by the FY 2016 NASA MAP Program: "Extremes in the Earth System", "Advanced methods for model evaluation", "Predictability in the Earth System" and "Coupling in the Earth System" By investigating how the regional drought extremes over the US may have changed in response to the observed long-term warming trend, the proposal also addresses one of the NASA's Science Mission Directorate (SMD) key questions: "How is the Earth system changing?" The expected outcome of the proposed work is an improved understanding of how reducing model mean bias in the GEOS-5 system affects the simulation and prediction of US drought. In addition, since many of the GEOS-5 model mean biases are similarly present in other models, our approach and findings should have broad applicability.
A detailed global representation of the chemical composition of the stratosphere, comprising available observations and advanced modeling is important for understanding stratospheric chemistry, radiative forcing and, indirectly, dynamics. The goal of this proposal is to develop a capability to assimilate satellite observations of water vapor, N2O, HCl, ClO, and HNO3 into the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation system integrated with a full stratospheric chemistry model in order to address the following science objectives:
This work will build on the success of the assimilation of retrieved ozone observations in GEOS-5, particularly in the MERRA-2 reanalysis. The required system development will include extending the assimilation capability to additional species, including stratospheric water vapor, chlorine and nitrogen compounds, and full integration of the existing stratospheric chemistry model (STRATCHEM) with the GEOS-5 data assimilation system. The proposed project will utilize the excellent record of atmospheric constituent observations from NASA satellite instruments, primarily from EOS Aura MLS (2004 to present). The expected deliverables will include a multiyear mini-reanalysis with observations of several ozone-related species and improved stratospheric forecasting capability. The results will be published in major peer-reviewed journals.
We propose to use onset transitions of short-term climate variability to evaluate new parameterizations in the GISS atmospheric general circulation model (AGCM) participating in the Coupled Model Intercomparison Project 6 (CMIP6). We focus on studying four natural climate transitions: the onset of active spells in the intraseasonal oscillation (ISO) of Indian summer monsoon (ISM), seasonal onsets of the ISM and West African monsoon (WAM), and the onset of strong El Niño events. These four natural phenomena involve significant changes in large-scale circulation and provide test beds for feedback mechanisms simulated by the climate model. Temporal evolution of climate variables relevant to cumulus, cloud microphysics, and boundary layer turbulence parameterizations as well as the prognostic precipitation scheme, together with atmospheric water vapor and heat budgets, will be investigated against MODIS, CloudSat/CALIPSO, and AIRS observed cloud properties, TRMM- and GPM-based estimates of precipitation and latent heating, and apparent heating and water sinks in MERRA-2 assimilated data.
Many climate model evaluations focus on comparison of climatologies from model simulations and observations by means of either long-term averages of climate variables or climatological relationships between climate variables. Other work uses natural climate variability to evaluate global climate sensitivities of individual feedback processes. However, the roles of each subgrid scale parameterization in controlling the model feedback mechanisms are essential in determining model discrepancies and must be assessed. In the proposed work, we utilize onset transitions of natural climate changes to assess the feedback processes through responses in parameterization schemes in the GISS CMIP6 model. Atmospheric water and energy budget analyses provide a means to investigate positive feedbacks that strengthen the onsets to the final mature active states, as well as negative feedbacks that prevent the transition processes from running away. We aim at achieving the following goals:
This proposal is proposed under Section 2 of NRA A13 "Advanced Methods for Model Evaluation". This proposal focuses on the onset transitions of four natural climate variations, with each having its own unique features for testing responses of model parameterizations to: