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NASA NTRSDate not supplied

Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS): Continuing the Legacy of SAGE Data Products

The Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument installed on the International Space Station (ISS) has completed almost half of a decade of data collection and production of science data products. The SAGE III/ISS is a solar and lunar occultation instrument that scans the light from the Sun and Moon through the limb of the Earth’s atmosphere to produce vertical profiles of aerosol, ozone, water vapor, and other trace gases. It continues the legacy of previous SAGE instruments dating back to the 1970s to provide data continuity of stratospheric constituents critical for assessing trends in the ozone layer. This presentation shows how SAGE III/ISS aerosol and gas vertical profiles continue to benefit a worldwide database of in situ and satellite data for climate observation.

Susan Kizer↗
NASA NTRS2023

An Evaluation of A Physics-Based Firn Model and A Semi-Empirical Firn Model Across the Greenland Ice Sheet (1980–2020)

The Greenland Ice Sheet's (GrIS) firn layer buffers the ice sheet's contribution to sea level rise by storing meltwater in its pore space. However, available pore space and meltwater retention capability is lost due to ablation of the firn layer and refreezing of meltwater as near-surface ice slabs in the firn. Understanding how firn properties respond to climate is important for constraining the GrIS's future contribution to sea level rise in a warming climate. Observations of firn density provide detailed information about firn properties, but they are spatially and temporally limited. Here we use two firn models, the physics-based SNOWPACK model and the Community Firn Model configured with a semi-empirical densification equation (CFM-GSFC), to quantify firn properties across the GrIS from 1980 through 2020. We use an identical forcing (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis) for SNOWPACK and the CFM-GSFC in order to isolate firn model differences. To evaluate the models, we compare simulated firn properties, including firn air content (FAC), to measurements from the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset of snow and firn density. Both models perform well (mean absolute percentage errors of 14 % in SNOWPACK and 16 % in the CFM-GSFC), though their performance is hindered by the spatial resolution of the atmospheric forcing. In the ice-sheet-wide simulations, the 1980–1995 average spatially integrated FAC (i.e., air volume in the firn) for the upper 100 m is 34 645 km 3 from SNOWPACK and 28 581 km 3 from the CFM-GSFC. The discrepancy in the magnitude of the modeled FAC stems from differences in densification with depth and variations in the sensitivity of the models to atmospheric forcing. In more recent years (2005–2020), both models simulate substantial depletion of pore space. During this period, the spatially integrated FAC across the entire GrIS decreases by 3.2 % (−66.6 km 3 yr −1 ) in SNOWPACK and 1.5 % (−17.4 km 3 yr −1 ) in the CFM-GSFC. These differing magnitudes demonstrate how model differences propagate throughout the FAC record. Over the full modeled record (1980–2020), SNOWPACK simulates a loss of pore space equivalent to 3 mm of sea level rise buffering, while the CFM-GSFC simulates a loss of 1 mm. The greatest depletion in FAC is along the margins and especially along the western margin where observations and models show the formation of near-surface, low-permeability ice slabs that may inhibit meltwater storage.

Megan Thompson-Munson↗
NASA NTRS2023

Importance of Internal Variability for Climate Model Assessment

Benchmarking climate model simulations against observations of the climate is core to the process of building realistic climate models and developing accurate future projections. However, in many cases, models do not match historical observations, particularly on regional scales. If there is a mismatch between modeled and observed climate features, should we necessarily conclude that our models are deficient? Using several illustrative examples, we emphasize that internal variability can easily lead to marked differences between the basic features of the model and observed climate, even when decades of model and observed data are available. This can appear as an apparent failure of models to capture regional trends or changes in global teleconnections, or simulation of extreme events. Despite a large body of literature on the impact of internal variability on climate, this acknowledgment has not yet penetrated many model evaluation activities, particularly for regional climate. We emphasize that using a single or small ensemble of simulations to conclude that a climate model is in error can lead to premature conclusions on model fidelity. A large ensemble of multidecadal simulations is therefore needed to properly sample internal climate variability in order to robustly identify model deficiencies and convincingly demonstrate progress between generations of climate models.

Climate and Earth system modelling↗
NASA NTRS2021

Land-Atmosphere Coupling at the U.S. Southern Great Plains: A Comparison on Local Convective Regimes Between ARM Observations, Reanalysis, and Climate Model Simulations

Using the 9-yr warm-season observations at the Atmospheric Radiation Measurement Southern Great Plains site, we assess the land-atmosphere (L-A) coupling in North American Regional Reanalysis (NARR) and two climate models: hindcasts with the Community Atmosphere Model version 5.1 by Cloud-Associated Parameterizations Testbed (CAM5-CAPT) and nudged runs with the Energy Exascale Earth System Model Atmosphere Model version 1 Regionally Refined Model (EAMv1-RRM). We focus on three local convective regimes and diagnose model behaviors using the Local Coupling metrics (Santanello et al. 2018). NARR agrees well with observations except a slightly warmer and drier surface with higher downwelling shortwave radiation and lower evaporative fraction. On clear-sky days, it shows warmer and drier early-morning conditions in both models with significant underestimates in surface evaporation by EAMv1-RRM. On the majority of the ARM-observed shallow cumulus days, there is no or little low-level clouds in either model. When captured in models, the simulated shallow cumulus shows much less cloud fraction and lower cloud bases than observed. On the days with late-afternoon deep convection, models tend to present a stable early-morning lower atmosphere more frequently than the observations, suggesting that the deep convection is triggered more often by elevated instabilities. Generally, CAM5-CAPT can reproduce the local L-A coupling processes to some extent due to the constrained early-morning conditions and large-scale winds. EAMv1-RRM exhibits large precipitation deficits and warm and dry biases towards mid-to-late summers, which may be an amplification through a positive L-A feedback among initial atmosphere and land states, convection triggering and large-scale circulations.

land-atmosphere↗
NASA NTRS2023

COSPAR Session on Space-Observation Contributions Supporting Climate Action

On Tuesday, 14 February 2023, COSPAR Commission A hosted a session in Vienna, Austria and virtually, on "Space-Observation Contributions Supporting Climate Action," as part of the United Nations Office for Outer Space Affairs (UNOOSA) Committee on the Peaceful Uses of Outer Space (UN COPUOS) Symposium on the Agenda for Sustainable Development.

Ralph Kahn↗
DOE OSTI2023

Assessment of CMIP5 and CMIP6 AMIP Simulated Clouds and Surface Shortwave Radiation Using ARM Observations over Different Climate Regions

The simulations of clouds and surface radiation from 10 CMIP6 models and their CMIP5 predecessors are compared to the ARM ground-based observations over different climate regions. Compared to the ARM radar-lidar derived total cloud fractions (CFT) and cloud fraction vertical distributions over the six selected sites, both CMIP5 and CMIP6 significantly underestimated CFT and low-level CF over the Northern Hemispheric midlatitude sites (SGPC1 and ENAC1), although the biases are generally smaller in CMIP6. Over the tropical oceanic site (TWPC2), 5 out of 10 CMIP6 models better simulated low-level CF than their CMIP5 predecessors. CMIP6 simulations generally agreed well with the ARM observations in CFT and cloud fraction vertical distributions over the tropical continental (MAOM1) and coastal (TWPC3) sites but missed the transitions between dry and wet seasons, similar to CMIP5 simulations. The improvements in downwelling shortwave fluxes (SWdn) at the surface from the majority of CMIP6 compared to CMIP5 primarily resulted from the improved cloud fraction simulations, especially over the SGPC1, ENAC1, and TWPC3 sites. By contrast, both CMIP5 and CMIP6 models exhibited diverse performances of clouds and shortwave radiation over the Arctic site (NSAC1), where CMIP6 models produced more clouds than CMIP5 models, especially for the low-level clouds. The comparisons between observations and CMIP5 and CMIP6 simulations provide valuable quantitative assessments of the accuracy of mean states and variabilities in the model simulations and shed light on general directions to improve climate models in different regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models

We present a new atmospheric river (AR) analysis and benchmarking tool, namely Atmospheric River Metrics Package (ARMP). It includes a suite of new AR metrics that are designed for quick analysis of AR characteristics via statistics in gridded climate datasets such as model output and reanalysis. This package can be used for climate model evaluation in comparison with reanalysis and observational products. Integrated metrics such as mean bias and spatial pattern correlation are efficient for diagnosing systematic AR biases in climate models. For example, the package identifies the fact that, in CMIP5 and CMIP6 (Coupled Model Intercomparison Project Phases 5 and 6) models, AR tracks in the South Atlantic are positioned farther poleward compared to ERA5 reanalysis, while in the South Pacific, tracks are generally biased towards the Equator. For the landfalling AR peak season, we find that most climate models simulate a completely opposite seasonal cycle over western Africa. This tool can also be used for identifying and characterizing structural differences among different AR detectors (ARDTs). For example, ARs detected with the Mundhenk algorithm exhibit systematically larger size, width, and length compared to the TempestExtremes (TE) method. The AR metrics developed from this work can be routinely applied for model benchmarking and during the development cycle to trace performance evolution across model versions or generations and set objective targets for the improvement of models. They can also be used by operational centers to perform near-real-time climate and extreme event impact assessments as part of their forecast cycle.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Introduction to our COSPAR-Sponsored Session: Space-Observation Contributions Supporting Climate Action

• The challenge for us at COSPAR in putting this program together was that we are primarily a scientifically oriented organization. “Climate Action,” as it is often expressed publicly, tends to be the domain of “activists.” As scientists, on the other hand, to retain our credibility – and for the contributions we make to continue carrying the weight of scientific truth – it is critical that we remain even-handed in our analysis and our reporting of results. • COSPAR is the Committee on Space Research, and fortunately, much of what we observe from space bears objectively on the attributes of the Earth System and how it is evolving. We can measure from space changes in the cryosphere, in the oceans, the land surface and the atmosphere, frequently, on a global scale, to a degree that is generally not possible any other way. And we have been making such observations, in increasing detail, scope, and precision, for about 60 years. • These data help us characterize the current state and identify patterns of change in the Earth System that allow us to infer processes and to improve climate models. With modeling we can test and refine our inferences, and make some predictions. The results can then inform policy and decision-makers, the public in general, as well as those taking direct action. • So, for today’s session we have invited 7 15-minute presentations spanning a broad range of climate-related topics, covering observations from space that can inform climate-related decision-making – from sea-ice extent and sea-level rise, to land surface properties and land-based water resources, to interactions between land or water surfaces and the atmosphere, to severe events in the atmosphere itself. • We begin with an overview of the observations contributed by one of the premier space agencies, given by NASA Chief Scientist and Senior Climate Advisor Dr. Kate Calvin, which will be followed by talks on what we are learning about specific attributes of the Earth System from space-based measurements that can inform climate-related policy and action.

Ralph Kahn↗
NASA NTRS2012

Satellite Observation Systems for Polar Climate Change Studies

The key observational tools for detecting large scale changes of various parameters in the polar regions have been satellite sensors. The sensors include passive and active satellite systems in the visible, infrared and microwave frequencies. The monitoring started with Tiros and Nimbus research satellites series in the 1970s but during the period, not much data was stored digitally because of limitations and cost of the needed storage systems. Continuous global data came about starting with the launch of ocean color, passive microwave, and thermal infrared sensors on board Nimbus-7 and Synthetic Aperture Radar, Radar Altimeter and Scatterometer on board SeaSat satellite both launched in 1978. The Nimbus-7 lasted longer than expected and provided about 9 years of useful data while SeaSat quit working after 3 months but provided very useful data that became the baseline for follow-up systems with similar capabilities. Over the years, many new sensors were launched, some from Japan Aeronautics and Space Agency (JAXA), some from the European Space Agency (ESA) and more recently, from RuSSia, China, Korea, Canada and India. For polar studies, among the most useful sensors has been the passive microwave sensor which provides day/night and almost all weather observation of the surface. The sensor provide sea surface temperature, precipitation, wind, water vapor and sea ice concentration data that have been very useful in monitoring the climate of the region. More than 30 years of such data are now available, starting with the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7, the Special Scanning Microwave/Imager (SSM/I) on board a Defense Meteorological Satellite Program (DMSP) and the Advanced Microwave Scanning Radiometer on board the EOS/ Aqua satellite. The techniques that have been developed to derive geophysical parameters from data provided by these and other sensors and associated instrumental and algorithm errors and validation techniques will be discussed. An important issue is the organization and storage of hundreds of terabytes of data collected by even just a few of these satellite sensors. Advances in mass storage and computer technology have made it possible to overcome many of the collection and archival problems and the availability of comprehensive satellite data sets put together by NASA's Earth Observing System project will be discussed.

Comiso, Josefino C.↗
NASA NTRS2014

Northern Late Winter Planetary Waves: MRO/MARCI Observations and Mars Climate Model Simulations

As does Earth, Mars presents pronounced global atmospheric circulation patterns. Solar differential heating drives mean meridional overturning (Hadley) circulations which are deep and intense, are hemispherically asymmetric, and where a cross-equatorial single cell dominates. Within middle and high latitudes, thermally indirect eddy-driven (Ferrel) circulation cells have been indicated. Differently, however, large-amplitude orography on planetary and continental scales on Mars can force very non-Earth-like hemispheric circulation patterns. Recent observations from the Mars Reconnaissance Orbiter, "Mars Color Imager" (MARCI) instrument are utilized that emphasize water ice clouds in ultra-violet (UV) wavelengths, and these measurements have been binned into "daily global maps" (DGMs) of water-ice cloud optical depth. The presence of large-scale, extratropical quasi-stationary atmospheric wave disturbances in middle and late winter of the northern hemisphere have been found to be present in such DGMs. In combination with such observations, a full-physics Mars global climate model (NASA ARC marsgcm 2.1) is applied to place the observations into context. During late northern winter, it is found that strong, forced Rossby modes (i.e., planetary waves) exist, and with direct correlation to columnintegrated cloud opacity undulating spatial patterns. At this season, zonal wavenumber s = 2 dominates (in contrast to wavenumber s = 1), consistent with MGS/TES analyses at this particular season (Banfield et al., 2003). Large-scale, planetary waves dictate the "coherence" of the northern polar vortex. Fundamentally, such forced planetary waves influence the polar vortex's impermeability (wave-induced) to tracer transport (e.g., dust and water-ice aerosol) and temporal mean water vapor spatial variations. The large-scale dynamical features of such planetary waves will be highlighted and discussed.

Northern polar vortex↗
NASA NTRS2023

Northeast Alaska Climate: Using Earth Observations to Evaluate Snow Variability through a Climatological Analysis to Support Ecological Monitoring in Northeast Alaska

Alaska is experiencing climate change at an unprecedented rate, with temperatures increasing twice as fast as the national average. The resulting changes to the landscape and ecosystems are significant, including shorter winters, declining snow depth, thawing permafrost, and rapidly receding glaciers. These changes are not only exacerbating the negative impacts of oil exploration but also affecting the food security of indigenous communities that rely on hunting as a subsistence food source. With the US Fish and Wildlife Service managing a potential tundra travel season for the first time in its history, adequate data on historic snow variables is essential to protect the unique habitat of the area. This project used NASA satellite and assimilation system data to inform and improve the current understanding of snow patterns in the Arctic National Wildlife Refuge and the National Petroleum Reserve – Alaska. The DEVELOP team used MODIS Normalized Difference Snow Index data to determine snow season duration, snow change frequency, and the first and last days of snow. The team also utilized the 2.1 Global Land Data Assimilation System and Daymet V4 products to study climatological trends in snow depth and snow water equivalent, respectively, across the study areas. The results of this study give users the capacity to visualize maps of multiple snow variables to monitor changes in snow conditions and proactively prepare for the ecological, cultural, and landscape impacts that changes in snow variability will cause in the future.

Remote Sensing↗
NASA NTRS2010

The Impact of Ocean Observations in Seasonal Climate Prediction

The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

Rienecker, Michele↗
NASA NTRS1994

A general circulation model study of the climatic effect of observed stratospheric ozone depletion between 1980 and 1990

The total ozone mapping spectrometer (TOMS) and stratospheric aerosol and gas experiment (SAGE) measurements show a significant reduction in the stratospheric ozone over the middle and high latitudes of both hemispheres between the years 1979 and 1991 (WMO, 1992). This change in ozone will effect both the solar and longwave radiation with climate implications. However, recent studies (Ramaswamy et al., 1992; WMO, 1992) indicate that the net effect depends not only on latitudes and seasons, but also on the response of the lower stratospheric temperature. In this study we use a general circulation model (GCM) to calculate the climatic effect due to stratospheric ozone depletion and compare the effect with that due to observed increases of trace gases CO2, CH4, N2O, and CFC's for the period 1980-1990. In the simulations, we use the observed changes in ozone derived from the TOMS data. The GCM used is a version of the NCAR community climate model referenced in Wang et al. (1991). For the present study we run the model in perpetual January and perpetual July modes in which the incoming solar radiation and climatological sea surface temperatures are held constant.

Dudek, Michael P.↗
NASA NTRS1993

Entropy and climate. I - ERBE observations of the entropy production of the earth

An approximate method for estimating the global distributions of the entropy fluxes flowing through the upper boundary of the climate system is introduced, and an estimate of the entropy exchange between the earth and space and the entropy production of the planet is provided. Entropy fluxes calculated from the Earth Radiation Budget Experiment measurements show how the long-wave entropy flux densities dominate the total entropy fluxes at all latitudes compared with the entropy flux densities associated with reflected sunlight, although the short-wave flux densities are important in the context of clear sky-cloudy sky net entropy flux differences. It is suggested that the entropy production of the planet is both constant for the 36 months of data considered and very near its maximum possible value. The mean value of this production is 0.68 x 10 exp 15 W/K, and the amplitude of the annual cycle is approximately 1 to 2 percent of this value.

Stephens, G. L.↗
NASA NTRS1986

Climate variability as observed by the Nimbus-7 ERB

Limits to the accuracy of the Earth Radiation Budget (ERB) data being obtained by the Nimbus-7 satellite are discussed with emphasis on the implications for the measured variabilities in the global climate. Error analyses are performed for both wide and narrow field of view instruments and the success of in-flight calibration efforts is noted. Alterations in the ERB due to the eruptions of El Chichon in 1982 and the 1982-1983 ENSO event are summarized, particularly the teleconnections which were observed during ENSO.

Ardanuy, P. E.↗
NASA NTRS1992

Validation of cloud forcing simulated by the National Center for Atmospheric Research Community Climate Model using observations from the Earth Radiation Budget Experiment

Satellite measurements of the effect of clouds on the top of atmosphere radiative energy budget are used to validate model simulations from the National Center for Atmospheric Research Community Climate Model (NCAR CCM). The ability of the NCAR CCM to reproduce the monthly mean global distribution and temporal variability on both daily and seasonal time scales is assessed. The comparison reveals several deficiencies in the CCM cloud representation. Most notable are the difficulties in properly simulating the effect of clouds on the planetary albedo. This problem arises from discrepancies in the model's portrayal of low-level cloudiness and leads to significant errors in the absorbed solar radiation simulated by the model. The CCM performs much better in simulating the effect of clouds on the longwave radiation emitted to space, indicating its relative success in capturing the vertical distribution of cloudiness. The daily variability of the radiative effects of clouds in both the shortwave and longwave spectral regions is systematically overestimated. Analysis of the seasonal variations illustrates a distinct lack of coupling in the seasonal changes in the radiative effects of cloudiness between the tropics and mid-latitudes and between the Northern and Southern Hemisphere. Much of this problem also arises from difficulties in simulating low-level cloudiness, placing further emphasis on the need for better model parameterizations of boundary layer clouds.

Soden, B. J.↗
NASA NTRSDate not supplied

GOES-16 GLM Observations for National Climate Assessments

The heating of air by a lightning discharge, followed by rapid cooling, leads to the production of lightning nitrogen oxides (“LNO x ” for brevity, where NO x = NO + NO 2 ). The LNO x are important trace gases because they control the concentration of the greenhouse gas ozone (O 3 ) and the highly reactive hydroxyl radicals (OH). In fact, LNO x are the most important source of NO x in the upper troposphere at tropical and subtropical latitudes, and climate is most sensitive to O 3 in the upper troposphere. Since lightning indirectly influences the Earth's climate, it is a key variable to monitor in National Climate Assessment (NCA) studies. In this work, GOES-16 Geostationary Lightning Mapper (GLM) flash counts and flash optical properties (i.e., optical energy, optical area, …) that are linked to LNO x production are examined in detail for the continental United States analysis region, and are compared with ground-based National Lightning Detection Network (NLDN) observations.

Lightning↗
NASA NTRS1994

Climatic consequences of observed ozone loss in the 1980s: Relevance to the greenhouse problem

Recently published findings using satellite and ground-based observations indicate a large winter and summertime decrease in the column abundance of ozone at high and middle latitudes during the last decade. Using a simple ozone depletion profile reflecting the observed decrease in ozone column abundance, Ramaswamy et al. (1992) showed that the negative radiative forcing that results from the ozone decrease between 1979 and 1990 approximately balanced the greenhouse climate forcing due to the chlorofluorocarbons emitted during the same period. Here, we extend the forcing analyses by calculating the equilibrium surface temperature response explicitly, using an updated version of the Atmospheric and Environmental Research two-dimensional radiative-dynamical seasonal model. The calculated steady state responses suggest that the surface cooling due to the ozone depletion in the lower stratosphere offsets about 30% of the surface warming due to greenhouse gases emitted during the same decade. The temperature offset is roughly a factor of 2 larger than the corresponding offset obtained from forcing intercomparisons. This result appears to be related to the climate feedback mechanisms operating in the model troposphere, most notably that associated with atmospheric meridional heat transport. Thus a comprehensive assessment of ozone change effects on the predicted greenhouse warming cannot be accomplished based on forcing evaluations alone. Our results also show that calculations adopting a seasonally and latitudinally dependent ozone depletion profile produce a negative forcing about 50% smaller than that calculated for the depletion profile used by Ramaswamy et al. (1992).

Molnar, G. I.↗