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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

The simulation of the seasonal cycle of the Southern Hemispheric circulation by the GLAS Seasonal Cycle Model and a comparison to observations

The general circulation of the Southern Hemisphere is quite different from that of the Northern Hemisphere in many important ways. These include the barotropic nature of the stationary waves and the presence of a strong barotropic component to the mean zonal wind, the lack of a strong seasonal dependence of the transient eddies, and the dominant role played by eddies with periods less than 10 days compared to longer period fluctuations. Such differences attest to the importance of the altered nature of the orographic and thermal land-sea forcings in the Southern Hemisphere compared to the Northern Hemisphere. Some of the important features of the Southern Hemisphere circulation as simulated by the GLAS Seasonal Cycle Model (SCM) are presented. The geographical patterns of local variability and their seasonal shifts in the SCM are discussed and compared to observations.

Straus, D. M.↗
NASA NTRS1983

On the role of the seasonal cycle

The definition of the seasonal cycle as the projection of an atmospheric time series onto a suitably defined subset of orthogonal basis functions guarantees that any atmospheric covariance may be expressed as the sum of a seasonal cycle part and a transient part (where transient refers to departures from the seasonal cycle rather than the time mean). Results are presented for the seasonal cycle contribution to the zonally averaged fluxes of momentum and heat, and for the zonally averaged height variance, in data for: the winter season, the winter season with the Fourier basis set appropriate for the seven-year time series, and the seven-year mean. The Fourier basis set calculation indicates that the interannual variability of the momentum flux is dominated by the interactions between the seasonal cycle and the meteorologically low frequency flow.

Straus, D. M.↗
NASA NTRS1981

The seasonal-cycle climate model

The seasonal cycle run which will become the control run for the comparison with runs utilizing codes and parameterizations developed by outside investigators is discussed. The climate model currently exists in two parallel versions: one running on the Amdahl and the other running on the CYBER 203. These two versions are as nearly identical as machine capability and the requirement for high speed performance will allow. Developmental changes are made on the Amdahl/CMS version for ease of testing and rapidity of turnaround. The changes are subsequently incorporated into the CYBER 203 version using vectorization techniques where speed improvement can be realized. The 400 day seasonal cycle run serves as a control run for both medium and long range climate forecasts alsensitivity studies.

Marx, L.↗
NASA NTRS2022

Changes in Anthropogenic Precursor Emissions Drive Shifts in the Ozone Seasonal Cycle Throughout the Northern Midlatitude Troposphere

Simulations by six Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models indicate that the seasonal cycle of baseline tropospheric ozone at northern midlatitudes has been shifting since the mid-20th century. Beginning in ∼ 1940, the magnitude of the seasonal cycle increased by ∼10 ppb (measured from seasonal minimum to maximum), and the seasonal maximum shifted to later in the year by about 3 weeks. This shift maximized in the mid-1980s, followed by a reversal – the seasonal cycle decreased in amplitude and the maximum shifted back to earlier in the year. Similar changes are seen in measurements collected from the 1970s to the present. The timing of the seasonal cycle changes is generally concurrent with the rise and fall of anthropogenic emissions that followed industrialization and the subsequent implementation of air quality emission controls. A quantitative comparison of the temporal changes in the ozone seasonal cycle at sites in both Europe and North America with the temporal changes in ozone precursor emissions across the northern midlatitudes found a high degree of similarity between these two temporal patterns. We hypothesize that changing precursor emissions are responsible for the shift in the ozone seasonal cycle; this is supported by the absence of such seasonal shifts in southern midlatitudes where anthropogenic emissions are much smaller. We also suggest a mechanism by which changing emissions drive the changing seasonal cycle: increasing emissions of NOx allow summertime photochemical production of ozone to become more important than ozone transported from the stratosphere, and increasing volatile organic compounds (VOCs) lead to progressively greater photochemical ozone production in the summer months, thereby increasing the amplitude of the seasonal ozone cycle. Decreasing emissions of both precursor classes then reverse these changes. The quantitative parameter values that characterize the seasonal shifts provide useful benchmarks for evaluating model simulations, both against observations and between models.

Ozone↗
NASA NTRS2012

A Technique Using Principal Component Analysis to Compare Seasonal Cycles of Earth Radiation from CERES and Model Computations

A method for quantitatively comparing the seasonal cycles of two global data sets is presented. The seasonal cycles of absorbed solar radiation (ASR) and outgoing longwave radiation (OLR) have been computed from an eight-year data set from the Clouds and Earth's Radiant Energy System (CERES) scanning radiometers and from a model data set produced by the NASA Goddard Space Flight Center Global Modeling and Assimilation Office. To compare the seasonal cycles from these two data sets, principal component (PC) analysis is used, where the PCs express the time variations and the corresponding empirical orthogonal functions (EOFs) describe the geographic variations. Ocean has a long thermal response time compared to land, so land and ocean are separated for the analysis. The root-mean square values for the seasonal cycles of ASR and OLR are extremely close for the two data sets. The first three PCs are quite close, showing that the time responses and magnitudes over the globe are very similar. The agreement between the two sets of PCs is quantified by computing the matrix of inner products of the two sets. For ASR over land, the first PCs of CERES and the model agree to better than 99.9%. The EOF maps are similar for most of the globe, but differ in a few places, and the agreement of the EOF maps is likewise quantified. Maps of differences between the annual cycles show regions of agreement and disagreement.

Smith, G. Louis↗
NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: an Application to Xco2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

segmentation algorithm↗
NASA NTRS1994

The roles of vertical mixing, solar radiation, and wind stress in a model simulation of the sea surface temperature seasonal cycle in the tropical Pacfic Ocean

The climatological seasonal cycle of sea surface temperature (SST) in the tropical Pacific is simulated using a newly developed upper ocean model. The roles of vertical mixing, solar radiation, and wind stress are investigated in a hierarchy of numerical experiments with various combinations of vertical mixing algorithms and surface-forcing products. It is found that the large SST annual cycle in the eastern equatorial Pacific is, to a large extent, controlled by the annually varying mixed layer depth which, in turn, is mainly determined by the competing effects of solar radiation and wind forcing. With the application of our hybrid vertical mixing scheme the model-simulated SST annual cycle is much improved in both amplitude and phase as compared to the case of a constant mixed layer depth. Beside the strong effects on vertical mixing, solar radiation is the primary heating term in the surface layer heat budget, and wind forcing influences SST by driving oceanic advective processes that redistribute heat in the upper ocean. For example, the SST seasonal cycle in the western Pacific basically follows the semiannual variation of solar heating, and the cycle in the central equatorial region is significantly affected by the zonal advective heat flux associated with the seasonally reversing South Equatorial Current. It has been shown in our experiments that the amount of heat flux modification needed to eliminate the annual mean SST errors in the model is, on average, no larger than the annual mean uncertainties among the various surface flux products used in this study. Whereas a bias correction is needed to account for remaining uncertainties in the annual mean heat flux, this study demonstrates that with proper treatment of mixed layer physics and realistic forcing functions the seasonal variability of SST is capable of being simulated successfully in response to external forcing without relying on a relaxation or damping formulation for the dominant surface heat flux contributions.

Chen, Dake↗
NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: An Application to XCO2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

Calle, Leonardo↗
NASA NTRS1990

The role of the seasonal cycle in the quasi-biennial oscillation of ozone

The evidence for seasonal synchronization of the ozone quasi-biennial oscillation (QBO) is reviewed, showing how this behavior is not expected on the basis of QBO transport alone but may arise due to the seasonal cycle. Near the equator, the ozone QBO is closely tied to the dynamical QBO and exhibits the same period as that oscillation. Maximum column ozone is attained soon after the transition to westerlies at 50 mb. In the subtropics, the ozone QBO is strongly tied to the seasonal cycle, with anomalies centered in the winter-spring season irrespective of QBO phase. These anomalies result from the interaction of the annual cycle and the QBO. Numerical model results are reported which establish that hemispheric asymmetry in the ozone QBO does not require seasonal asymmetries in the strength or location of the Hadley circulation. The fundamental cause of hemispheric asymmetry in the subtropical ozone QBO is the timing of equatorial, ozone anomalies relative to the seasonal cycle.

Gray, Lesley J.↗
NASA NTRS1988

Observations and wind-forced model simulations of the mean seasonal cycle in tropical Pacific sea surface topography

This paper quantitatively evaluates simulations of the mean seasonal cycle in the tropical Pacific Ocean, using a multiple vertical-mode version of the Busalacchi and O'Brien (1980) linear model. The period studied (1979-1981) includes several coincident oceanic and surface wind data sets, as well as a fairly regular seasonal cycle leading into the 1982-1983 El Nino. Simulated mean seasonal cycles in dynamic height and sea level are compared with observed variations in expendable bathythermograph and island tide gauge data averaged over the same period. It was found that, although all simulations show characteristic features of the mean meridional ridge-trough structure in surface topography, simulated north and south equatorial ridges at 20 deg N and 20 deg S are much higher than those observed. Only weak equatorial ridges were generated near 4 N, and none of the simulations exhibited a significant equatorial trough. These discrepancies are attributed to limitations in model physics and to the wind forcing.

Mcphaden, Michael J.↗
NASA NTRS2023

Asymmetry in the Seasonal Cycle of Zonal-Mean Surface Air Temperature

At most latitudes, the seasonal cycle of zonal-mean surface air temperature is notably asymmetric: the length of the warming season is not equal to the length of the cooling season. The asymmetry varies spatially, with the cooling season being ∼40 days shorter than the warming season in the subtropics and the warming season being ∼100 days shorter than the cooling season at the poles. Furthermore, the asymmetry differs between the Northern Hemisphere and the Southern Hemisphere. Here, we show that these observed features are broadly captured in a simple model for the evolution of temperature forced by realistic insolation. The model suggests that Earth's orbital eccentricity largely determines the hemispheric contrast, and obliquity broadly dictates the meridional structure. Clouds, atmospheric heat flux convergence, and time-invariant effective surface heat capacity have minimal impacts on seasonal asymmetry. This simple, first-order picture has been absent from previous discussions of the surface temperature seasonal cycle.

seasonal cycle↗
NASA NTRS1987

Simulations of the HDO and H2O-18 atmospheric cycles using the NASA GISS general circulation model - The seasonal cycle for present-day conditions

The cycles of the water isotopic species (HDO and H2O-18) have been incorporated into the NASA Goddard Institute for Space Studies atmospheric general circulation model (GCM). The results of a three-year simulation for present-day conditions are discussed, with special emphasis on the comparison between predicted and observed isotopic distributions for both the seasonal and annual time scales. The observed seasonal cycles are generally well simulated. For the annual scale the observed linear relationship between delta O-18 and the surface temperature at middle and high latitudes, as well as the absence of any correlation between these fields in tropical and equatorial regions, are properly obeyed by the GCM simulation. In the tropical and equatorial regions the delta O-18 patterns for both observations and the GCM are influenced by the amount of rainfall. There is excellent agreement between the simulated and observed delta D-delta O-18 relationship throughout the world.

Jouzel, J.↗
NASA NTRS2023

Importance of Atmospheric Feedbacks in Simulating the Seasonal Cycle of the Antarctic Sea Ice and Its Response to Perturbations

The seasonal cycle of the Antarctic sea ice extent is largely controlled by the evolution of the insolation received at the top of the atmosphere. However, sea ice processes and feedbacks with the ocean and the atmosphere can modulate this seasonal cycle. Here, the atmospheric feedbacks are quantified in a series of idealized sensitivity experiments performed with an eddy-permitting (1/4°) NEMO-LIM3 Southern Ocean configuration, including a representation of ice shelf cavities, in which the model was either driven by an atmospheric reanalysis or coupled to the COSMO-CLM 2 regional atmospheric model. In these experiments, sea ice thermodynamics and dynamics as well as the exchanges with the ocean and atmosphere are strongly perturbed. This perturbation is achieved by modifying snow and ice thermal conductivities, the vertical mixing in the ocean top layers, the effect of freshwater uptake/release upon sea ice growth/melt, ice dynamics and surface albedo. We show that the changes in surface heat fluxes are very different between the configurations driven by the reanalysis and those coupled to the atmosphere. Atmospheric feedbacks enhance the response of the modelled winter ice extent to any of the perturbed processes, and the enhancement is strongest when the albedo is modified. The response of sea ice volume and extent to changes in entrainment of subsurface warm waters to the ocean surface is also greatly amplified by the coupling with the atmosphere. By contrast, the atmospheric feedbacks can damp the impact of the perturbations affecting the heat conductivity through sea ice.

Atmospheric feedbacks↗
NASA NTRS1991

What causes the seasonal cycle of stationary waves in the southern stratosphere?

Stationary planetary waves in the southern stratosphere display a characteristic seasonal cycle. Previous research based on a one-dimensional model suggests that this behavior is mainly determined by seasonally varying transmission properties of the atmosphere with respect to wave propagation. The issue is investigated with the help of a hemispheric, linear, quasi-geostrophic model. It reproduces well some of the observed qualitative features and is internally consistent in the sense that its seasonal wave cycle can be explained in terms of varying wave transmission properties of the mean circulation. On the other hand, the model does not yield the observed seasonal cycle. Despite considerable sensitivity to modifications in the basic-state wind and dissipation parameterization, the model could not be reasonably fit to reproduce the observed seasonal cycle. Possible reasons for the model deficiency are put forward. In summary, even though suggestive, the present study is not entirely conclusive about the degree to which the observed cycle is determined by wave transmission properties alone.

Wirth, Volkmar↗
NASA NTRS1992

Modeling the seasonal cycle of CO2 on Mars: A fit to the Viking lander pressure curves

We have constructed a more accurate Mars thermal model, similar to the one used by Leighton and Murray in 1966, which solves radiative, conductive, and latent heat balance at the surface as well as the one-dimensional heat conduction equation for 40 layers to a depth of 15 meters every 1/36 of a Martian day. The planet is divided into 42 latitude bands with a resolution of two degrees near the poles and five degrees at lower latitudes, with elevations relative to the 6.1 mbar reference areoid. This estimate of the Martian zonally averaged topography was derived primarily from radio occultations. We show that a realistic one-dimensional thermal model is able to reproduce the VL1 pressure curve reasonably well without having to invoke complicated atmospheric effects such as dust storms and polar hoods. Although these factors may cause our deduced values for each model parameter to differ from its true value, we believe that this simple model can be used as a platform to study many aspects of the Martian CO2 cycle over seasonal, interannual, and long-term climate timescales.

Wood, S. E.↗
NASA NTRS2014

The Seasonal Cycle of Satellite Chlorophyll Fluorescence Observations and its Relationship to Vegetation Phenology and Ecosystem Atmosphere Carbon Exchange

Mapping of terrestrial chlorophyll uorescence from space has shown potentialfor providing global measurements related to gross primary productivity(GPP). In particular, space-based fluorescence may provide information onthe length of the carbon uptake period that can be of use for global carboncycle modeling. Here, we examine the seasonal cycle of photosynthesis asestimated from satellite fluorescence retrievals at wavelengths surroundingthe 740nm emission feature. These retrievals are from the Global OzoneMonitoring Experiment 2 (GOME-2) flying on the MetOp A satellite. Wecompare the fluorescence seasonal cycle with that of GPP as estimated froma diverse set of North American tower gas exchange measurements. Because the GOME-2 has a large ground footprint (40 x 80km2) as compared with that of the flux towers and requires averaging to reduce random errors, we additionally compare with seasonal cycles of upscaled GPP in the satellite averaging area surrounding the tower locations estimated from the Max Planck Institute for Biogeochemistry (MPI-BGC) machine learning algorithm. We also examine the seasonality of absorbed photosynthetically-active radiation(APAR) derived with reflectances from the MODerate-resolution Imaging Spectroradiometer (MODIS). Finally, we examine seasonal cycles of GPP as produced from an ensemble of vegetation models. Several of the data-driven models rely on satellite reflectance-based vegetation parameters to derive estimates of APAR that are used to compute GPP. For forested sites(particularly deciduous broadleaf and mixed forests), the GOME-2 fluorescence captures the spring onset and autumn shutoff of photosynthesis as delineated by the tower-based GPP estimates. In contrast, the reflectance-based indicators and many of the models tend to overestimate the length of the photosynthetically-active period for these and other biomes as has been noted previously in the literature. Satellite fluorescence measurements therefore show potential for improving model GPP estimates.

Joiner, J.↗
NASA NTRS1993

The seasonal cycle of low stratiform clouds

The seasonal cycle of low stratiform clouds is studied using data from surface-based cloud climatologies. The impact of low clouds on the radiation budget is illustrated by comparison of data from the Earth Radiation Budget Experiment with the cloud climatologies. Ten regions of active stratocumulus convection are identified. These regions fall into four categories: subtropical marine, midlatitude marine, Arctic stratus, and Chinese stratus. With the exception of the Chinese region, all the regions with high amounts of stratus clouds are over the oceans. In all regions except the Arctic, the season of maximum stratus corresponds to the season of greatest lower-troposphere static stability. Interannual variations in stratus cloud amount also are related to changes in static stability. A linear analysis indicates that a 6 percent increase in stratus fractional area coverage is associated with each 1 C increase in static stability. Over midlatitude oceans, sky-obscuring fog is a large component of the summertime stratus amount. The amount of fog appears to be related to warm advection across sharp gradients of SST.

Klein, Stephen A.↗
NASA NTRS1985

The Seasonal Cycle of Storminess as Measured by Band-Pass Fluctuations

A sample of statistics, namely the seasonal cycle of baroclinic storms, as represented by bandpass filtered geopotential height variances at 850 mb is presented. The particular filter used is that suggested by Blackmon and White (1982), and retains periods of approximately 2.5 to 10 days. The time series of height (at each grid point) were filtered by removing the annual and semiannual cycles for that point, and by removing zonal wavenumbers higher than 20. The bandpass filter was then applied. The height variances of the filtered fields were then computed for each winter season, each spring, each summer season, and each fall season. These variances were then averaged by season. Maps of the standard deviation are shown. Figures show clearly the seasonal cycle of bandpass fluctuations. The major seasonal variation is seen to consist mostly of a summertime weakening and shift; spring and fall appear nearly identical to winter. The corresponding results at 500 mb are similar, but with the stormtrack variance being slightly larger in spring and fall compared to winter.

Straus, D. M.↗