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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

A Coupled Ocean General Circulation, Biogeochemical, and Radiative Model of the Global Oceans: Seasonal Distributions of Ocean Chlorophyll and Nutrients

A coupled ocean general circulation, biogeochemical, and radiative model was constructed to evaluate and understand the nature of seasonal variability of chlorophyll and nutrients in the global oceans. Biogeochemical processes in the model are determined from the influences of circulation and turbulence dynamics, irradiance availability. and the interactions among three functional phytoplankton groups (diatoms. chlorophytes, and picoplankton) and three nutrients (nitrate, ammonium, and silicate). Basin scale (greater than 1000 km) model chlorophyll results are in overall agreement with CZCS pigments in many global regions. Seasonal variability observed in the CZCS is also represented in the model. Synoptic scale (100-1000 km) comparisons of imagery are generally in conformance although occasional departures are apparent. Model nitrate distributions agree with in situ data, including seasonal dynamics, except for the equatorial Atlantic. The overall agreement of the model with satellite and in situ data sources indicates that the model dynamics offer a reasonably realistic simulation of phytoplankton and nutrient dynamics on synoptic scales. This is especially true given that initial conditions are homogenous chlorophyll fields. The success of the model in producing a reasonable representation of chlorophyll and nutrient distributions and seasonal variability in the global oceans is attributed to the application of a generalized, processes-driven approach as opposed to regional parameterization and the existence of multiple phytoplankton groups with different physiological and physical properties. These factors enable the model to simultaneously represent many aspects of the great diversity of physical, biological, chemical, and radiative environments encountered in the global oceans.

Gregg, Watson W.↗
NASA NTRS1999

Seasonal Distributions of Global Ocean Chlorophyll and Nutrients: Analysis with a Coupled Ocean General Circulation Biogeochemical, and Radiative Model

A coupled general ocean circulation, biogeochemical, and radiative model was constructed to evaluate and understand the nature of seasonal variability of chlorophyll and nutrients in the global oceans. The model is driven by climatological meteorological conditions, cloud cover, and sea surface temperature. Biogeochemical processes in the model are determined from the influences of circulation and turbulence dynamics, irradiance availability, and the interactions among three functional phytoplankton groups (diatoms, chorophytes, and picoplankton) and three nutrient groups (nitrate, ammonium, and silicate). Phytoplankton groups are initialized as homogeneous fields horizontally and vertically, and allowed to distribute themselves according to the prevailing conditions. Basin-scale model chlorophyll results are in very good agreement with CZCS pigments in virtually every global region. Seasonal variability observed in the CZCS is also well represented in the model. Synoptic scale (100-1000 km) comparisons of imagery are also in good conformance, although occasional departures are apparent. Agreement of nitrate distributions with in situ data is even better, including seasonal dynamics, except for the equatorial Atlantic. The good agreement of the model with satellite and in situ data sources indicates that the model dynamics realistically simulate phytoplankton and nutrient dynamics on synoptic scales. This is especially true given that initial conditions are homogenous chlorophyll fields. The success of the model in producing a reasonable representation of chlorophyll and nutrient distributions and seasonal variability in the global oceans is attributed to the application of a generalized, processes-driven approach as opposed to regional parameterization, and the existence of multiple phytoplankton groups with different physiological and physical properties. These factors enable the model to simultaneously represent the great diversity of physical, biological, chemical, and radiative environments encountered in the global oceans.

Gregg, Watson W.↗
NASA NTRS2005

Interannual Variability of Seasonal Pblooms in the North Polar Atlantic in Response to Atmospheric Forcing

The year-to-year variability of timing, intensity, and spatial distribution of surface phytoplankton during spring-summer seasons is examined in the north polar region of the Atlantic using satellite-derived chlorophyll a concentration (Chl) over 7 years (1998 2004). Each year phytoplankton bloom differed in onset, temporal evolution, and intensity. This interannual variability is to a large degree controlled by local weather, as indicated by the analysis of coincident meteorological data. The atmospheric forcing was parameterized in terms of the generation rate of turbulent kinetic energy (TKE(sub RT)) supplied from the atmosphere to the ocean. Timing of the bloom is delayed in years with high TKE(sub RT) supplied to the ocean in March. In April, Chl (local and regionally averaged) and TKE(sub RT) are inversely related to one another. The late winter and early spring atmospheric conditions influence the seasonal Chl patterns. The seasonal (April-August) Chl correlates well with net heat flux, wind energy, and TKE(sub RT) in March and April, but the correlation can be negative or positive in different areas of the north polar Atlantic. The correlation between TKE(sub RT) and seasonal Chl is positive in the Greenland Gyre (higher seasonal Chl corresponds to higher TKE(sub RT) in March April) and negative in the regions of East Greenland and North Atlantic/West Spitsbergen Currents. The most likely explanation for the positive correlation between TKE(sub RT) and Chl within the Greenland Gyre is that higher TKE(sub RT) in spring increases the seasonal supply of nutrients into surface waters.

Stramska, Malgorzata↗
NASA NTRS1989

Arctic sea ice 1973-1987 - Seasonal, regional, and interannual variability

The seasonal, regional, and interannual variations in the Northern-Hemisphere sea-ice cover were investigated for the time period 1973-1987, using data derived from the Nimbus-5 ESMR and the Nimbus-7 SMMR. The records show an average seasonal cycle of sea-ice cover, ranging from a summer minimum of 8.5 x 10 to the 6th sq km in September to a winter maximum of 15 x 10 to the 6th sq km in March, with considerable interannual variability found both regionally and hemispherically. Some regions (e.g., the Baffin Bay/Davis Strait, were found to exhibit upward trends in sea-ice extents, while other regions (e.g., the Kara Sea and the Barents Sea) exhibited downward trend. However, the record for the Northern Hemisphere overall showed no significant trend over the 1973-1987 period, giving no evidence of consistent warming or cooling of the north polar region.

Parkinson, Claire L.↗
NASA NTRS2012

Arctic Ocean Freshwater: How Robust are Model Simulations

The Arctic freshwater (FW) has been the focus of many modeling studies, due to the potential impact of Arctic FW on the deep water formation in the North Atlantic. A comparison of the hindcasts from ten ocean-sea ice models shows that the simulation of the Arctic FW budget is quite different in the investigated models. While they agree on the general sink and source terms of the Arctic FW budget, the long-term means as well as the variability of the FW export vary among models. The best model-to-model agreement is found for the interannual and seasonal variability of the solid FW export and the solid FW storage, which also agree well with observations. For the interannual and seasonal variability of the liquid FW export, the agreement among models is better for the Canadian Arctic Archipelago (CAA) than for Fram Strait. The reason for this is that models are more consistent in simulating volume flux anomalies than salinity anomalies and volume-flux anomalies dominate the liquid FW export variability in the CAA but not in Fram Strait. The seasonal cycle of the liquid FW export generally shows a better agreement among models than the interannual variability, and compared to observations the models capture the seasonality of the liquid FW export rather well. In order to improve future simulations of the Arctic FW budget, the simulation of the salinity field needs to be improved, so that model results on the variability of the liquid FW export and storage become more robust.

Jahn, A.↗
NASA NTRS2001

Modeling Modern Methane Emissions from Natural Wetlands: Interannual Variations 1982-1993 - 2

A global run of a process-based methane model [Walter et al., this issue] is performed using high-frequency atmospheric forcing fields from ECMWF reanalyses of the period from 1982 to 1993. We calculate global annual methane emissions to be 260 Tg/ yr. 25% of methane emissions originate from wetlands north of 30 deg. N. Only 60% of the produced methane is emitted, while the rest is re-oxidized. A comparison of zonal integrals of simulated global wetland emissions and results obtained by an inverse modeling approach shows good agreement. In a test with data from two wetlands, the seasonality of simulated and observed methane emissions agrees well. The effects of sub-grid scale variations in model parameters and input data are examined. Modeled methane emissions show high regional, seasonal and interannual variability. Seasonal cycles of methane emissions are dominated by temperature in high latitude wetlands, and by changes in the water table in tropical wetlands. Sensitivity tests show that +/- 1 C changes in temperature lead to +/- 20 % changes in methane emissions from wetlands. Uniform changes of +/- 20% in precipitation alter methane emissions by about +/- 18%. Limitations in the model are analyzed. Simulated interannual variations in methane emissions from wetlands are compared to observed atmospheric growth rate anomalies. Our model simulation results suggest that contributions from other sources than wetlands and/or the sinks are more important in the tropics than north-of 30 deg. N. In higher northern latitudes, it seems that a large part, of the observed interannual variations can be explained by variations in wetland emissions. Our results also suggest that reduced wetland emissions played an important role in the observed negative methane growth rate anomaly in 1992.

Walter, Bernadette P.↗
NASA NTRS2006

Contrasts in Sea Ice Formation and Production in the Arctic Seasonal and Perennial Ice Zones

Four years (1997-2000) of RADARSAT Geophysical Processor System (RGPS) data are used to contrast the sea ice deformation and production regionally, and in the seasonal (SIZ) and perennial (PIZ) ice zones. Ice production is of seasonal ice in openings during the winter. 3-day estimates of these quantities are provided within Lagrangian elements initially 10 km on a side. A distinct seasonal cycle is seen in both zones with these estimates highest in the late fall and with seasonal minimums in the mid-winter. Regional divergence over the winter could be up to 30%. Spatially, the highest deformation is in the SIZ north of coastal Alaska. Both ice deformation and production are higher in the SIZ: deformation-related ice production in the SIZ (approx.0.5 m) is 1.5-2.3 times that of the PIZ (approx.0.3 m) - this is connected to ice strength and thickness. Atmospheric forcing and boundary layer structure contribute to only the seasonal and interannual variability. Seasonal ice growth in ice fractures accounts for approx.25-40% of the total ice production of the Arctic Ocean. By itself, this deformation-ice production relationship could be considered a negative feedback when thickness is perturbed. However, the overall effect on ice production in the face of increasing seasonal and thinner/weaker ice coverage could be modified by: local destabilization of the water column promoting overturning of warmer water due to increased brine rejection; and, the upwelling of the pynocline associated with increased occurrence of large shear motion in sea ice.

Kwok, R.↗
NASA NTRS2006

Contrasts in Sea Ice Deformation and Production in the Arctic Seasonal and Perennial Ice Zones

Four years (1997-2000) of RADARSAT Geophysical Processor System (RGPS) data are used to contrast the sea ice deformation and production regionally, and in the seasonal (SIZ) and perennial (PIZ) ice zones. Ice production is of seasonal ice in openings during the winter. Three-day estimates of these quantities are provided within Lagrangian elements initially 10 km on a side. A distinct seasonal cycle is seen in both zones with these estimates highest in the late fall and with seasonal minimums in the midwinter. Regional divergence over the winter could be up to 30%. Spatially, the highest deformation is seen in the SIZ north of coastal Alaska. Both ice deformation and production are higher in the SIZ: deformation-related ice production in the SIZ (approx.0.5 m) is 1.5-2.3 times that of the PIZ (approx.0.3 m): this is connected to ice strength and thickness. Atmospheric forcing and boundary layer structure contribute to only the seasonal and interannual variability. Seasonal ice growth in ice fractures accounts for approx.25-40% of the total ice production of the Arctic Ocean. Uncertainties in these estimates are discussed. By itself, this deformation-ice production relationship could be considered a negative feedback when thickness is perturbed. However, the overall effect on ice production in the face of increasing seasonal and thinner/weaker ice coverage could be modified by local destabilization of the water column promoting overturning of warmer water due to increased brine rejection; and the upwelling of the pynocline associated with increased occurrence of large shear motion in sea ice. Divergence is shown to be negligibly correlated to cyclonic motion in summer and winter in both ice zones.

Arctic Ocean↗
NASA NTRS2002

Derivation of Tropospheric Column Ozone from the EPTOMS/GOES Co-Located Data Sets using the Cloud Slicing Technique

A recently developed technique called cloud slicing used for deriving upper tropospheric ozone from the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) instrument combined together with temperature-humidity and infrared radiometer (THIR) is no longer applicable to the Earth Probe TOMS (EPTOMS) because EPTOMS does not have an instrument to measure cloud top temperatures. For continuing monitoring of tropospheric ozone between 200-500hPa and testing the feasibility of this technique across spacecrafts, EPTOMS data are co-located in time and space with the Geostationary Operational Environmental Satellite (GOES)-8 infrared data for 2001 and early 2002, covering most of North and South America (45S-45N and 120W-30W). The maximum column amounts for the mid-latitudinal sites of the northern hemisphere are found in the March-May season. For the mid-latitudinal sites of the southern hemisphere, the highest column amounts are found in the September-November season, although overall seasonal variability is smaller than those of the northern hemisphere. The tropical sites show the weakest seasonal variability compared to higher latitudes. The derived results for selected sites are cross validated qualitatively with the seasonality of ozonesonde observations and the results from THIR analyses over the 1979-1984 time period due to the lack of available ozonesonde measurements to study sites for 2001. These comparisons show a reasonably good agreement among THIR, ozonesonde observations, and cloud slicing-derived column ozone. With very limited co-located EPTOMS/GOES data sets, the cloud slicing technique is still viable to derive the upper tropospheric column ozone. Two new variant approaches, High-Low (HL) cloud slicing and ozone profile derivation from cloud slicing are introduced to estimate column ozone amounts using the entire cloud information in the troposphere.

Ahn, C.↗
NASA NTRS2007

Dynamic and Regression Modeling of Ocean Variability in the Tide-Gauge Record at Seasonal and Longer Periods

Comparison of monthly mean tide-gauge time series to corresponding model time series based on a static inverted barometer (IB) for pressure-driven fluctuations and a ocean general circulation model (OM) reveals that the combined model successfully reproduces seasonal and interannual changes in relative sea level at many stations. Removal of the OM and IB from the tide-gauge record produces residual time series with a mean global variance reduction of 53%. The OM is mis-scaled for certain regions, and 68% of the residual time series contain a significant seasonal variability after removal of the OM and IB from the tide-gauge data. Including OM admittance parameters and seasonal coefficients in a regression model for each station, with IB also removed, produces residual time series with mean global variance reduction of 71%. Examination of the regional improvement in variance caused by scaling the OM, including seasonal terms, or both, indicates weakness in the model at predicting sea-level variation for constricted ocean regions. The model is particularly effective at reproducing sea-level variation for stations in North America, Europe, and Japan. The RMS residual for many stations in these areas is 25-35 mm. The production of "cleaner" tide-gauge time series, with oceanographic variability removed, is important for future analysis of nonsecular and regionally differing sea-level variations. Understanding the ocean model's strengths and weaknesses will allow for future improvements of the model.

Hill, Emma M.↗
NASA NTRS2015

Multimodel Ensembles of Wheat Growth: Many Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

model intercomparison↗
NASA NTRS2015

Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

wheat↗
NASA NTRS2024

Evaluating Differences Among Crop Models in Simulating Soybean in-Season Growth

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and “blind” evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8–31% after Full calibration). However, the nRMSE for pod weight (of 17–64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47–93 days) and effective seed filling period (18–54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.

Agricultural Model Intercomparison and Improvement↗
NASA NTRS2022

Winds at the Mars 2020 Landing Site: 1. Near-Surface Wind Patterns at Jezero Crater

This is the first part of a two-part paper. NASA's Mars 2020 Perseverance rover measured winds on the Jezero crater floor close to the delta of an ancient river. A mostly repeatable diurnal cycle was observed and presented two regimes: (a) a convective regime, from dawn to sunset, with average easterly to southeasterly winds, during which maximum wind speeds were measured, and (b) a nighttime regime with westerly-northwesterly winds followed by a relatively calm period with highly variable wind directions as a function of sol and time of night. The timing and magnitude of the observed regimes are consistent with primary control by regional and local slope flows. Data suggest that the surface circulation at Jezero region in northern spring and summer is highly unaffected by large-scale circulation except during particular periods in the diurnal cycle or generally during dust storms, which is supported by MarsWRF model simulations. Consequently, the observed seasonal variability was weak. However, sol-to-sol and seasonal variability were measured, most of it during certain nighttime periods. Traveling waves consistent with baroclinic instability were clearly observed in surface winds at L_s ∼ 75°. The early MY36/2022A regional dust storm at L_s ∼ 153° disturbed the wind patterns with changes suggesting enhanced tidal flows. After sunset, the dust storm also produced detectable gravity wave activity, increasing the mixing in the nighttime planetary boundary layer during storm conditions. Inferred wind directions from dust devil movies strongly suggest that prevailing winds continued to be slope-driven during the late summer, fall and early winter seasons.

D. Viúdez-Moreiras↗
NASA NTRS2010

Warm Season Subseasonal Variability and Climate Extremes in the Northern Hemisphere: The Role of Stationary Rossby Waves

This study examines the nature of boreal summer subseasonal atmospheric variability based on the new NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) for the period 1979-2010. An analysis of the June, July and August subseasonal 250hPa v-wind anomalies shows distinct Rossby wave-like structures that appear to be guided by the mean jets. On monthly subseasonal time scales, the leading waves (the first 10 rotated empirical orthogonal functions or REOFs of the 250hPa v-wind) explain about 50% of the Northern Hemisphere vwind variability, and account for more than 30% (60%) of the precipitation (surface temperature) variability over a number of regions of the northern middle and high latitudes, including the U.S. northern Great Plains, parts of Canada, Europe, and Russia. The first REOF in particular, consists of a Rossby wave that extends across northern Eurasia where it is a dominant contributor to monthly surface temperature and precipitation variability, and played an important role in the 2003 European and 2010 Russian heat waves. While primarily subseasonal in nature, the Rossby waves can at times have a substantial seasonal mean component. This is exemplified by REOF 4 which played a major role in the development of the most intense anomalies of the U.S. 1988 drought (during June) and the 1993 flooding (during July), though differed in the latter event by also making an important contribution to the seasonal mean anomalies. A stationary wave model (SWM) is used to reproduce some of the basic features of the observed waves and provide insight into the nature of the forcing. In particular, the responses to a set of idealized forcing functions are used to map the optimal forcing patterns of the leading waves. Also, experiments to reproduce the observed waves with the SWM using MERRA-based estimates of the forcing indicate that the wave forcing is dominated by sub-monthly vorticity transients.

Schubert, Siegfried↗
NASA NTRS2021

Illinois Disasters: Utilizing NASA Earth Observations to Enhance Drought Monitoring in Illinois

Drought and flooding in Illinois have severe impacts on the communities and ecosystems of the state. Soil moisture is a valuable indicator of drought and flood vulnerability but can be difficult to measure since in situ monitoring is limited to discrete stations throughout the state. The team created a framework to compare in situ, modeled, and NASA satellite soil moisture measurements to increase the spatial coverage of soil moisture monitoring. The team partnered with the Illinois State Weather Survey, USDA Midwest Climate Hub, NOAA Regional Climate Services of the Central Region, NOAA National Integrated Drought Information System’s Midwest Drought Early Warning System, and the NOAA North Central River ForecastCenter. The team standardized and compared soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission, modeled soil moisture outputs from NASA’s SPoRT Land Information System (SPoRT-LIS), and in situ measurements from the Illinois State Weather Survey’s Water and Atmospheric Resources Monitoring (WARM) program. Compared to the WARM data, the satellite and modeled data showed seasonally variable differences and bias. The difference was highest in the winter months and lowest in the late summer and early fall months for the SMAP and SPoRT-LIS data products. SPoRT-LIS produced lower seasonal variability and SMAP demonstrated higher correlation values and lower differences. These analyses suggest that both SMAP and SPoRT-LIS products offer unique strengths and limitations when used for soil moisture monitoring

Joshua Green↗
NASA NTRS2000

"Cloud Slicing" : A New Technique to Derive Tropospheric Ozone Profile Information from Satellite Measurements

A new technique denoted cloud slicing has been developed for estimating tropospheric ozone profile information. All previous methods using satellite data were only capable of estimating the total column of ozone in the troposphere. Cloud slicing takes advantage of the opaque property of water vapor clouds to ultraviolet wavelength radiation. Measurements of above-cloud column ozone from the Nimbus 7 total ozone mapping spectrometer (TOMS) instrument are combined together with Nimbus 7 temperature humidity and infrared radiometer (THIR) cloud-top pressure data to derive ozone column amounts in the upper troposphere. In this study tropical TOMS and THIR data for the period 1979-1984 are analyzed. By combining total tropospheric column ozone (denoted TCO) measurements from the convective cloud differential (CCD) method with 100-400 hPa upper tropospheric column ozone amounts from cloud slicing, it is possible to estimate 400-1000 hPa lower tropospheric column ozone and evaluate its spatial and temporal variability. Results for both the upper and lower tropical troposphere show a year-round zonal wavenumber 1 pattern in column ozone with largest amounts in the Atlantic region (up to approx. 15 DU in the 100-400 hPa pressure band and approx. 25-30 DU in the 400-1000 hPa pressure band). Upper tropospheric ozone derived from cloud slicing shows maximum column amounts in the Atlantic region in the June-August and September-November seasons which is similar to the seasonal variability of CCD derived TCO in the region. For the lower troposphere, largest column amounts occur in the September-November season over Brazil in South America and also southern Africa. Localized increases in the tropics in lower tropospheric ozone are found over the northern region of South America around August and off the west coast of equatorial Africa in the March-May season. Time series analysis for several regions in South America and Africa show an anomalous increase in ozone in the lower troposphere around the month of March which is not observed in the upper troposphere. The eastern Pacific indicates weak seasonal variability of upper, lower, and total tropospheric ozone compared to the western Pacific which shows largest TCO amounts in both hemispheres around spring months. Ozone variability in the western Pacific is expected to have greater variability caused by strong convection, pollution and biomass burning, land/sea contrast and monsoon developments.

Ziemke, J. R.↗