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NASA NTRS2018

A Reconstruction of Global Hydroclimate and Dynamical Variables Over the Common Era

Hydroclimate extremes critically affect human and natural systems, but there remain many unanswered questions about their causes and how to interpret their dynamics in the past and in climate change projections. These uncertainties are due, in part, to the lack of long-term, spatially resolved hydroclimate reconstructions and information on the underlying physical drivers for many regions. Here we present the first global reconstructions of hydroclimate and associated climate dynamical variables over the past two thousand years. We use a data assimilation approach tailored to reconstruct hydroclimate that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere?ocean climate model. The global reconstructions are annually or seasonally resolved and include two spatiotemporal drought indices, near-surface air temperature, an index of North Atlantic variability, the location of the intertropical convergence zone, and monthly Nino indices. This database, called the Paleo Hydrodynamics Data Assimilation product (PHYDA), will provide a critical new platform for investigating the causes of past climate variability and extremes, while informing interpretations of future hydroclimate projections.

Natural hazards↗

DOE OSTI2021

Evaluating bloom potential of the green-tide forming alga Ulva ohnoi under ocean acidification and warming

Herein we discuss the occurrence of green-tides, whose bloom potential may be increased by various human activities and biogeochemical process, results in enormous economic losses and ecosystem collapse. In this study, we investigated the ecophysiology of the subtropical green-tide forming alga, Ulva ohnoi complex (hereafter: U. ohnoi), under simulated future ocean conditions in order to predict its bloom potential using photosynthesis and growth measurements, and stable isotope analyses. Our mesocosm system included four experimental conditions that simulated the individual and combined effects of elevated CO 2 and temperature, namely control (450 μatm CO 2 & 20 °C), acidification (900 μatm CO 2 & 20 °C), warming (450 μatm CO 2 & 25 °C), and greenhouse (900 μatm CO 2 & 25 °C). Photosynthetic electron transport rates (rETR) increased significantly under acidification conditions, but net photosynthesis and growth were not affected. In contrast, rETR, net photosynthesis, and growth all decreased significantly under elevated temperature conditions (i.e. both warming and greenhouse). These results represent the imbalance of energy metabolism between electron transport and O 2 production that may be expected under ocean acidification conditions. This imbalance appears to be related to carbon and nitrogen assimilation by U. ohnoi. In particular, 13 C and 15 N discrimination data suggest U. ohnoi prefers CO 2 and NH 4 + over HCO 3 - and NO 3 - as sources of carbon and nitrogen, respectively, and this results in increased N content in the thallus under ocean acidification conditions. Together, our results suggest a trade-off in which the bloom potential of U. ohnoi could increase under ocean acidification due to greater N accumulation and through the saving of energy during carbon and nitrogen metabolism, but that elevated temperatures could decrease U. ohnoi's bloom potential through a decrease in photosynthesis and growth.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

The remote sensing of oceanic primary productivity - A review

The ocean is a major sink for atmospheric carbon dioxide. This paper assesses the correctness of the present estimates of the marine primary productivity, obtained by remote sensing techniques, by modeling the physiological mechanisms of carbon assimilation by phytoplankton. The model uses, as the input, measurements of the ocean-surface pigments and data on the incident solar irradiance, and incorporates a description of the photosynthetically available and usable irradiance in the ocean, to describe the light field required for photosynthesis. A comparison of measured and estimated values of the water-column integrated primary marine productivity over large ocean regions yielded favorable results. These estimates do not appear to depend on regional or seasonal factors.

Collins, Donald J.↗

NASA NTRS1993

A preliminary study of the impact of the ERS 1 C band scatterometer wind data on the European Centre for Medium-Range Weather Forecasts global data assimilation system

A preliminary assessment of the impact of the ERS 1 scatterometer wind data on the current European Centre for Medium-Range Weather Forecasts analysis and forecast system has been carried out. Although the scatterometer data results in changes to the analyses and forecasts, there is no consistent improvement or degradation. Our results are based on comparing analyses and forecasts from assimilation cycles. The two sets of analyses are very similar except for the low level wind fields over the ocean. Impacts on the analyzed wind fields are greater over the southern ocean, where other data are scarce. For the most part the mass field increments are too small to balance the wind increments. The effect of the nonlinear normal mode initialization on the analysis differences is quite small, but we observe that the differences tend to wash out in the subsequent 6-hour forecast. In the Northern Hemisphere, analysis differences are very small, except directly at the scatterometer locations. Forecast comparisons reveal large differences in the Southern Hemisphere after 72 hours. Notable differences in the Northern Hemisphere do not appear until late in the forecast. Overall, however, the Southern Hemisphere impacts are neutral. The experiments described are preliminary in several respects. We expect these data to ultimately prove useful for global data assimilation.

Hoffman, Ross N.↗

NASA NTRS2017

The Cyclone Global Navigation Satellite System (CYGNSS) - Analysis and Data Assimilation for Tropical Convection

Cyclone Global Navigation Satellite System (CYGNSS): a constellation of 8 micro-satellite observatories launched in November 2016, to measure near-surface oceanic wind speed. Main goal: To monitor surface wind fields of the Tropical Cyclones' inner core, including regions beneath the intense eye wall and rain bands that could not previously be measured from space; Cover 38 deg S -38 deg N with unprecedented temporal resolution and spatial coverage, under all precipitating conditions Low flying satellite: Pass over ocean surface more frequently than one large satellite. A median(mean) revisit time of 2.8(7.2) hrs.

CYGNSS↗

NASA NTRS2000

Evaluation of Bogus Vortex Techniques with Four-Dimensional Variational Data Assimilation

The effectiveness of techniques for creating "bogus" vortices in numerical simulations of hurricanes is examined by using the Penn State/NCAR nonhydrostatic mesoscale model (MM5) and its adjoint system. A series of four-dimensional variational data assimilation (4-D VAR) experiments is conducted to generate an initial vortex for Hurricane Georges (1998) in the Atlantic Ocean by assimilating bogus sea-level pressure and surface wind information into the mesoscale numerical model. Several different strategies are tested for improving the vortex representation. The initial vortices produced by the 4-D VAR technique are able to reproduce many of the structural features of mature hurricanes. The vortices also result in significant improvements to the hurricane forecasts in terms of both intensity and track. In particular, with assimilation of only bogus sea-level pressure information, the response in the wind field is contained largely within the divergent component, with strong convergence leading to strong upward motion near the center. Although the intensity of the initial vortex seems to be well represented, a dramatic spin down of the storm occurs within the first 6 h of the forecast. With assimilation of bogus surface wind data only, an expected dominance of the rotational component of the wind field is generated, but the minimum pressure is adjusted inadequately compared to the actual hurricane minimum pressure. Only when both the bogus surface pressure and wind information are assimilated together does the model produce a vortex that represents the actual intensity of the hurricane and results in significant improvements to forecasts of both hurricane intensity and track.

Pu, Zhao-Xia↗

NASA NTRS2024

Revisiting the petrogenesis of pyroclastic glass bead deposits at the Apollo 15 and 17 sites

The Apollo 15 low-titanium and Apollo 17 high-titanium pyroclastic glass beads are amongst the most primitive magmatically derived samples obtained from the Moon. Two key samples, the low-Ti Apollo 15426 green glass clod and the high-Ti Apollo 74220 orange glass are morphologically distinct, where the Apollo 15 beads are larger (~ 107 μm along maximum axis) and more fractured, and the Apollo 17 are smaller (~42 μm) and less fractured. In this study, holohyaline beads as well as crystallized beads were examined from both samples. Crystallized beads show compositional variability in major, minor, and trace elements and enable examination of magmatic mineral fractionation processes during cooling of both deposits. The Apollo 15426 beads experienced variable olivine crystallization, whereas the Apollo 74220 beads experienced both olivine and ilmenite crystallization. Holohyaline beads from both deposits show more limited major, minor, and trace element variability than their crystallized counterparts. Trace element abundance data for individual holohyaline beads show that in Apollo 74220, they are tightly clustered at ~30 × Carbonaceous Ivuna chondrite [CI] with negative Eu anomalies and subchondritic Nb/Ta, and interpreted to reflect the presence of late-stage magma ocean cumulate overturned into an otherwise primitive mantle source. Incompatible trace element abundances for holohyaline beads in 15426 are supra-chondritic from ~8 × CI, to >80 × CI, with pronounced relative depletions in Sr and Eu for the most incompatible element enriched beads, which represent a distinct bead group within the deposit. Apollo 15426 beads have elevated Ni and Co abundances at the edges of the beads compared to their centers. These data are interpreted to reflect a more complex magmatic evolution of the 15426 deposit, beginning with: (i) initial magma generation, storage and assimilation within shallower low-Ca and high-Ca pyroxene bearing magma ocean cumulates (15B,C); (ii) mobilization of the earlier magmas by more recently generated primitive magmas (15A); (iii) eruption and crystallization of some beads (15D,E), and (iv) later jumbling of the deposit, possible impact contamination and addition of exotic bead components (J Group). In contrast, the 74220 data shows no discernable difference between Ni and Co abundances at the edges and centers supporting prior observations for limited melt fractionation and an absence of meteoritic components. It is further suggested that both deposits are likely to have been formed in the presence of a transient atmosphere. Using 74220 melt compositions from this study, post-entrapment crystallization abundances range from 266 to 1130 micrograms/g for H 2 O, 36 to 68 micrograms/g for F, 441 to 832 micrograms/g for S, and zero to 2.31 micrograms/g for Cl, consistent with prior studies and suggesting up to ~0.1 wt.% H 2 O in the melt, with considerably less in the source. The role that late-stage magma ocean cumulates rich in ilmenite, apatite and high-Ca pyroxene might play in modifying this volatile element estimate, however, casts remaining doubt on the volatile element abundance and evolution of the primitive Moon.

Moon↗

NASA NTRS2008

Assessment of Global Annual Atmospheric Energy Balance from Satellite Observations

Global atmospheric energy balance is one of the fundamental processes for the earth's climate system. This study uses currently available satellite data sets of radiative energy at the top of atmosphere (TOA) and surface and latent and sensible heat over oceans for the year 2000 to assess the global annual energy budget. Over land, surface radiation data are used to constrain assimilated results and to force the radiation, turbulent heat, and heat storage into balance due to a lack of observation-based turbulent heat flux estimations. Global annual means of the TOA net radiation obtained from both direct measurements and calculations are close to zero. The net radiative energy fluxes into the surface and the surface latent heat transported into the atmosphere are about 113 and 86 Watts per square meter, respectively. The estimated atmospheric and surface heat imbalances are about -8 ~ 9 Watts per square meter, values that are within the uncertainties of surface radiation and sea surface turbulent flux estimates and likely systematic biases in the analyzed observations. The potential significant additional absorption of solar radiation within the atmosphere suggested by previous studies does not appear to be required to balance the energy budget the spurious heat imbalances in the current data are much smaller (about half) than those obtained previously and debated at about a decade ago. Progress in surface radiation and oceanic turbulent heat flux estimations from satellite measurements significantly reduces the bias errors in the observed global energy budgets of the climate system.

Lin, Bing↗

NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗

NASA NTRS2017

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP.

CDA↗

NASA NTRS1998

Ocean Thermal and Color Evolution During the 1997/1998 ENSO Event

A reduced gravity primitive equation modeling and assimilation system is used to study the evolution of the tropical Pacific during the 1997/1998 ENSO cycle. The modeling/assimilation scheme ingests satellite altimeter data and TAO temperature profiles and uses SSM/I satellite derived winds as surface boundary forcing. The four-dimensional structure of the upper ocean circulation structure will be compared against available in situ observations across the Pacific basin. In particular, variability near the Galapagos Islands will be highlighted during the spring of 1998 when phytoplankton concentrations were observed to increase a hundred-fold over a two week period.

Rienecker, Michele↗

NASA NTRS1995

Mean Sea Level Derived from Altimetry and Wind-Driven Numerical Models in the Indian Ocean

Wind-driven model skill in simulating sea level variations in the Indian Ocean depends on our knowledge of the mean ocean dynamic topography. This is demonstrated by running the nonlinear or linear version of a shallow-water model driven by observed winds over Geosat and TOPEX periods. Geosat variations are assimilated in the nonlinear shallow-water model with the objective of obtaining topography data.

numerical models Indian ocean mean sea level remot↗

NASA NTRS2000

Advanced Sounders on EOS

NASA's Earth Observing System is a series of three satellites that carry advanced instruments to observe and monitor the global atmosphere, ocean, and land surface. In this talk, I will describe the instruments and how they might be exploited in data assimilation.

Joiner, J.↗

NASA NTRS2014

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.

Blankenship, Clay B.↗

NASA NTRSDate not supplied

Tracking 2020 Decreases in Carbon Dioxide Due to the COVID19 Pandemic in NASA’s GEOS Modeling system: Implications for Space-Based Carbon Monitoring

The COVID19 pandemic led to abrupt, worldwide changes in human activity and related emissions of air pollutants and greenhouse gases that are unprecedented in modern times. NASA satellites have demonstrated their ability to observe some of these impacts, particularly in relation to short-lived gases like nitrogen dioxide (NO2), which is emitted primarily from transportation. Bottom-up analyses of carbon dioxide (CO2) emissions suggest that the growth of atmospheric CO2 has also slowed, but differences are much more subtle than for NO2 because of the lifetime of atmospheric CO2 and sectoral differences in emission reductions. Estimates of CO2 from NASA’s Orbiting Carbon Observatory 2 (OCO-2) provide a unique view of COVID19 impacts, but observed changes in the column-average CO2 can be difficult to interpret because of gaps in spatial coverage. Assimilating these data into the Goddard Earth Observing System (GEOS), an integrated Earth system model with an advanced Constituent Data Assimilation System (CoDAS), helped to reveal changes in CO2 that are consistent with separate, bottom-up analyses of emissions reductions. Both indicate that from February-April of 2020, the growth in CO2 over Europe, North America, and Asia was roughly 0.3 ppm less than during the previous four years. Anomalies derived from gap-filled GEOS OCO-2 CoDAS products contribute to a joint effort by the world’s space agencies to track COVID19 impacts on the Earth. However, attribution of these changes is complicated by interannual variability in atmospheric circulation and the influence of climate on ocean and land carbon sinks. We discuss these results from the perspective of space-based carbon monitoring, which has received considerable support over the past decade from NASA. Our results demonstrate the accomplishments of current sensors and data assimilation systems, but also highlight challenges in providing high quality, low latency information to the public. In particular, understanding and attributing CO2 changes during 2020 requires year-specific information about land and ocean fluxes, which is often delayed for months or even years. We discuss current limitations and potential solutions to address these lags, which would support more reliable and timely space-based carbon monitoring.

COVID-19↗

NASA NTRS2017

Data Assimilation as it Relates to the Sea Ice Outlook (SIO) and Prospects for Improvement

Improved seasonal forecasts of Arctic sea ice are important for regional stakeholders, but also for obtaining a better understanding of the Arctic climate system. An important part of the forecasts is the initial sea ice, ocean, and atmosphere initial conditions. I briefly give an overview of the initial conditions currently being used in seasonal sea ice predictions. I also identify available sources of observational data and prospects for coupled atmosphere/ocean reanalyses.

Cullather, Richard↗

NASA NTRS1985

Development and testing of a simple assimilation technique to derive average wind fields from simulated scatterometer data

A simple algorithm is developed and tested to derive a regularly spaced wind field in a limited area from simulated multiorbit scatterometer data. The data are generated by sampling a time-varying known wind field, the 1000-mb FGGE data, from a simulated scatterometer. A simple assimilation technique is used to derive a regularly spaced (100-km grid) wind field representation of two-day averages from the simulated data. Several test cases are considered, and it is noted that the assimilation technique might be applicable to large-scale ocean or atmospheric models.

Legler, D. M.↗

NASA NTRS2011

Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Rudlosky, Scott D.↗