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NASA NTRS2011

An Estimate of the North Atlantic Basin Tropical Cyclone Activity for the 2011 Hurricane Season

Estimates are presented for the expected level of tropical cyclone activity for the 2011 North Atlantic Basin hurricane season. It is anticipated that the frequency of tropical cyclones for the North Atlantic Basin during the 2011 hurricane season will be near to above the post-1995 means. Based on the Poisson distribution of tropical cyclone frequencies for the current more active interval 1995-2010, one computes P(r) = 63.7% for the expected frequency of the number of tropical cyclones during the 2011 hurricane season to be 14 plus or minus 3; P(r) = 62.4% for the expected frequency of the number of hurricanes to be 8 plus or minus 2; P(r) = 79.3% for the expected frequency of the number of major hurricanes to be 3 plus or minus 2; and P(r) = 72.5% for the expected frequency of the number of strikes by a hurricane along the coastline of the United States to be 1 plus or minus 1. Because El Nino is not expected to recur during the 2011 hurricane season, clearly, the possibility exists that these seasonal frequencies could easily be exceeded. Also examined are the effects of the El Nino-Southern Oscillation phase and climatic change (global warming) on tropical cyclone seasonal frequencies, the variation of the seasonal centroid (latitude and longitude) location of tropical cyclone onsets, and the variation of the seasonal peak wind speed and lowest pressure for tropical cyclones.

Wilson, Robert M.↗

NASA NTRS2014

Aerosol Seasonal Variations over Urban-Industrial Regions in Ukraine According to AERONET and POLDER Measurements

The paper presents an investigation of aerosol seasonal variations in several urban-industrial regions in Ukraine. Our analysis of seasonal variations of optical and physical aerosol parameters is based on the sun-photometer 2008-2013 data from two urban ground-based AERONET (AErosol RObotic NETwork) sites in Ukraine (Kyiv, Lugansk) as well as on satellite POLDER instrument data for urban-industrial areas in Ukraine. We also analyzed the data from one AERONET site in Belarus (Minsk) in order to compare with the Ukrainian sites. Aerosol amount and optical depth (AOD) values in the atmosphere columns over the large urbanized areas like Kyiv and Minsk have maximum values in the spring (April-May) and late summer (August), whereas minimum values are observed in late autumn. The results show that fine-mode particles are most frequently detected during the spring and late summer seasons. The analysis of the seasonal AOD variations over the urban-industrial areas in the eastern and central parts of Ukraine according to both ground-based and POLDER data exhibits the similar traits. The seasonal variation similarity in the regions denotes the resemblance in basic aerosol sources that are closely related to properties of aerosol particles. The behavior of basic aerosol parameters in the western part of Ukraine is different from eastern and central regions and shows an earlier appearance of the spring and summer AOD maxima. Spectral single-scattering albedo, complex refractive index and size distribution of aerosol particles in the atmosphere column over Kyiv have different behavior for warm (April-October) and cold seasons. The seasonal features of fine and coarse aerosol particle behavior over the Kyiv site were analyzed. A prevailing influence of the fine-mode particles on the optical properties of the aerosol layer over the region has been established. The back-trajectory and cluster analysis techniques were applied to study the seasonal back trajectories and prevailing directions of the arrived air mass for the Kyiv and Minsk sites.

Ukraine↗

NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗

NASA NTRS2018

Climate Shifts Within Major Agricultural Seasons for +1.5 and +2.0 °C Worlds: HAPPI Projections and AgMIP Modeling Scenarios

This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO2 concentration that corresponds to the +1.5 °C World but overestimates the CO2 concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO2 on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5 °C and +2.0 °C worlds for adaptation planning in these climate stabilization targets.

Alexander C. Ruane↗

NASA NTRS2022

Seasonal Tidewater Glacier Terminus Oscillations Bias Multi-Decadal Projections of Ice Mass Change

Numerical, process-based simulations of tidewater glacier evolution are necessary to project future sea-level change under various climate scenarios. Previous work has shown that nonlinearities in tidewater glacier and ice stream dynamics can lead to biases in simulated ice mass change in the presence of noisy forcings. Ice sheet modeling projections that are used in the IPCC Assessment Report 6 (AR6) utilize atmospheric and oceanic forcings at annual temporal resolution, omitting any higher frequency forcings. Here, we quantify the effect of seasonal (<1 year) tidewater glacier terminus oscillations on decadal-scale (30 years) mass change. We use an idealized geometry to mimic realistic tidewater glacier geometries and investigate the impact of the magnitude of seasonal oscillations, bed slope at the glacier terminus, and basal friction law. We find that omitting seasonal terminus motion results in biased mass change projections, with up to an 39% underestimate or 25% overestimate of mass loss when seasonality is neglected, depending on bed slope and magnitude of oscillations. The bias is most sensitive to the magnitude of the seasonal terminus oscillations and exhibits very little sensitivity to choice of friction law. Although omitting seasonal terminus motion may bias mass change projections of individual glaciers, further work is needed to investigate the impact on ice sheet mass change projections. In order to achieve this, seasonality in atmospheric and oceanic forcings must be adequately represented and observations of seasonal terminus positions and tidewater glacier thickness changes must be acquired to evaluate numerical models. Note: Zip folder contains all videos listed in the supplemental document and can be downloaded for viewing.

D. Felikson↗

DOE OSTI2024

The Modeled Seasonal Cycles of Surface N 2 O Fluxes and Atmospheric N 2 O

Nitrous oxide (N 2 O) is a greenhouse gas and stratospheric ozone-depleting substance with large and growing anthropogenic emissions. Previous studies identified the influx of N 2 O-depleted air from the stratosphere to partly cause the seasonality in tropospheric N 2 O (aN 2 O), but other contributions remain unclear. Here, we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N 2 O Model Intercomparison Project with tropospheric transport modeling to simulate aN 2 O at eight remote air sampling sites for modern and pre-industrial periods. Models show general agreement on the seasonal phasing of zonal-average N 2 O fluxes for most sites, but seasonal peak-to-peak amplitudes differ several-fold across models. The modeled seasonal amplitude of surface aN 2 O ranges from 0.25 to 0.80 ppb (interquartile ranges 21%–52% of median) for land, 0.14–0.25 ppb (17%–68%) for ocean, and 0.28–0.77 ppb (23%–52%) for combined flux contributions. The observed seasonal amplitude ranges from 0.34 to 1.08 ppb for these sites. The stratospheric contributions to aN 2 O, inferred by the difference between the surface-troposphere model and observations, show 16%–126% larger amplitudes and minima delayed by ~1 month compared to Northern Hemisphere site observations. Land fluxes and their seasonal amplitude have increased since the pre-industrial era and are projected to grow further under anthropogenic activities. Our results demonstrate the increasing importance of land fluxes for aN 2 O seasonality. Considering the large model spread, in situ aN 2 O observations and atmospheric transport-chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting carbon-nitrogen cycles under ongoing global warming.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1991

Seasonal variations in urinary risk factors among patients with nephrolithiasis

Twenty-four hour urine specimens from 5,677 stone-forming patients throughout the United States were analyzed for seasonal variations in urinary risk factors for nephrolithiasis. Determinations were performed for urine volume, pH, calcium, oxalate, phosphorus, sodium, magnesium, citrate, sulfate, uric acid, and the relative supersaturation (RS) of calcium oxalate, brushite, monosodium urate, and uric acid. Criteria for significant seasonal variation included a significant difference in monthly means of risk factors, seasonal grouping of the data by the Student-Newman-Keuls multiple range test, consistent year-to-year trends and a physiologically significant range. Minimum urine volume of 1.54 +/- 0.70 SD L/day occurred in October while a maximum urine volume of 1.76 +/- 0.78 SD L/day was observed during February. Minimum urine pH of 5.94 +/- 0.64 SD was observed during July and August while a maximum pH of 6.18 +/- 0.61 SD was observed during February. Daily urinary excretion of sodium was lowest during August, 158 +/- 74 SD mEq/day and highest during February 177 +/- 70 SD mEq/day. The RS of brushite and uric acid were found to display significant pH-dependent seasonal variation with a maximum RS of uric acid 2.26 +/- 1.98 SD in June and a low of 1.48 +/- 1.30 SD in February. Maximum RS of brushite 2.75 +/- 2.58 was observed during February. Minimum RS of brushite 1.93 +/- 1.70 SD was observed in June. Phosphorus excretion displayed seasonal variation about a spring-fall axis with a maximum value 1042 +/- 373 SD mg/day in April and a minimum value of 895 +/- 289 SD mg/day. Urine volume, sodium, and pH were significantly lower during the summer (June, July, August) than in the winter (December, January, February). The RS of uric acid was higher, but that of brushite and monosodium urate was lower in the summer than in the winter. The seasonal changes observed in urine volume, pH, sodium, and the RS of brushite and uric acid are consistent with summertime sweating and increased physical activity. Seasonal variations in phosphorus excretion are probably dietary in origin. The summertime was characterized by an increased propensity for the crystallization of uric acid but not of calcium oxalate or calcium phosphate.

NASA Discipline Regulatory Physiology↗

NASA NTRS2014

Amazon Forests Maintain Consistent Canopy Structure and Greenness During the Dry Season

The seasonality of sunlight and rainfall regulates net primary production in tropical forests. Previous studies have suggested that light is more limiting than water for tropical forest productivity, consistent with greening of Amazon forests during the dry season in satellite data.We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area or leaf reflectance, using a sophisticated radiative transfer model and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability.

dry season↗

DOE OSTI2025

Hot season gets hotter due to rainfall delay over tropical land in a warming climate

Tropical land generally experiences the hottest period (spring) in a year just before the onset of wet season. Previous studies suggested that in a warming climate, the wet season would come later, but its origin is debated and its impact on temperature remains unknown. Here, we find that the warming of hot season would be amplified under global warming, and refer to it as “hot-season-gets-hotter” phenomenon. The amplified hot season warming is closely tied to the amplified warming of hot temperature percentiles. The hot-season-gets-hotter phenomenon is mainly due to the rainfall delay and most evident in the Amazon, where spring is warming by almost 1?K more than the annual mean and the 99th percentile temperatures are warming ~30% more than the mean by the end of 21st century in a high emission scenario. Comparing experiments with and without land-atmosphere coupling, it is further found that the rainfall delay is initially driven by the enhanced effective atmospheric heat capacity and then substantially amplified by positive soil moisture-atmosphere feedback. In the satellite period, observations consistently show that the hot-season-gets-hotter phenomenon has already emerged along with the rainfall delay in the Amazon. Intensified hot and dry spring climate can enhance risks of drought, heatwaves and wildfires, threatening the Amazon forest and habitats in the tropics.

54 ENVIRONMENTAL SCIENCES↗

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 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.↗

NASA NTRS1994

Seasonal trend analysis of published ground-based and TOMS total ozone data through 1991

A seasonal trend analysis of published Dobson (including stations' newly revised and Brewer-simulated Dobson) total ozone data through 1991 from a network of 56 stations has been performed, using three different data periods. The trend results for the longest data period 1964 - 1991 indicate substantial negative trends in ozone in the higher northern latitudes during the winter and spring seasons, some evidence of negative trend in the higher southern latitudes (30 deg S - 55 deg S) during all seasons, and trends close to zero for all seasons over the 30 deg S - 30 deg N latitude range. For the shortest data period, November 1978 through 1991, there is a clear indication that trends have become more negative in the higher northern latitudes, especially during the winter and spring seasons, and also in the higher southern latitudes in all seasons. A seasonal trend analysis of zonal averages of total ozone mapping spectrometer (TOMS) satellite total ozone data for the comparable period November 1978 through 1991 has also been performed, and moderately good agreement is found between trends in Dobson and TOMS data over this period.

Reinsel, Gregory C.↗

NASA NTRS1996

Detection of an ENSO Signal in Seasonal Length-of-Day Variations

Conservation of angular momentum dictates that as the wind-driven axial atmospheric angular momentum changes, so will the length-of-day (LOD). In particular, as the strength of the seasonal zonal winds change, so should the strength of the seasonal LOD signals. Here, observed changes in the strengths of the annual and semiannual LOD signals during 1963-1991 are analyzed and shown to be both significantly correlated (at the 99% significance level) with the Southern Oscillation Index (SOI), and to exhibit trends of comparable magnitude but opposite signs. This reported correlation between the SOI and changes in the amplitude of the seasonal LOD signals demonstrates a linkage between seasonal LOD (and hence seasonal zonal wind) variability and the El Nino / Southern Oscillation (ENSO) phenomenon. Furthermore, this study suggests that observed variations in the amplitudes of the seasonal LOD signals can be used to study changes in the strengths of the seasonal atmospheric zonal winds on interannual to decadal and longer time scales.

Gross, Richard S.↗

NASA NTRS1997

Comparison of the Seasonal Change in Cloud-Radiative Forcing from Atmospheric General Circulation Models and Satellite Observations

We compare seasonal changes in cloud-radiative forcing (CRF) at the top of the atmosphere from 18 atmospheric general circulation models, and observations from the Earth Radiation Budget Experiment (ERBE). To enhance the CRF signal and suppress interannual variability, we consider only zonal mean quantities for which the extreme months (January and July), as well as the northern and southern hemispheres, have been differenced. Since seasonal variations of the shortwave component of CRF are caused by seasonal changes in both cloudiness and solar irradiance, the latter was removed. In the ERBE data, seasonal changes in CRF are driven primarily by changes in cloud amount. The same conclusion applies to the models. The shortwave component of seasonal CRF is a measure of changes in cloud amount at all altitudes, while the longwave component is more a measure of upper level clouds. Thus important insights into seasonal cloud amount variations of the models have been obtained by comparing both components, as generated by the models, with the satellite data. For example, in 10 of the 18 models the seasonal oscillations of zonal cloud patterns extend too far poleward by one latitudinal grid.

Cess, R. D.↗

NASA NTRS2001

Trends in the Length of the Southern Ocean Sea Ice Season: 1979-1999

Satellite data can be used to observe the sea ice distribution around the continent of Antarctica on a daily basis and hence to determine how many days a year have sea ice at each location. This has been done for each of the 21 years 1979-1999. Mapping the trends in these data over the 21-year period reveals a detailed pattern of changes in the length of the sea ice season around Antarctica. Most of the Ross Sea ice cover has undergone a lengthening of the sea ice season, whereas most of the Amundsen Sea ice cover and almost the entire Bellingshausen Sea ice cover have undergone a shortening of the sea ice season. Results around the rest of the continent, including in the Weddell Sea, are more mixed, but overall, more of the Southern Ocean experienced a lengthening of the sea ice season than a shortening. For instance, the area experiencing a lengthening of the sea ice season by at least 1 day per year is 5.8 x 10(exp 6) sq km, whereas the area experiencing a shortening of the sea ice season by at least 1 day per year is less than half that, at 2.8 x 10(exp 6) sq km. This contrasts sharply with what is happened over the same period in the Arctic, where, overall, there has been some depletion of the ice cover, including shortened sea ice seasons and decreased ice extents.

Parkinson, Claire L.↗

NASA NTRS2001

Seasonal Rates for Atlantic Basin Tropical Cyclones During the Present Epoch

Ten-year moving averages of the seasonal rates for "named storms," tropical storms, hurricanes, and major (or intense) hurricanes in the Atlantic basin since 1950 suggest that the present epoch is one of enhanced activity. Consequently, the outlook for the 2001 hurricane season and immediately succeeding seasons is for all categories of Atlantic basin tropical cyclones to have seasonal rates at levels equal to or above their long-term median rates, especially when the season is designated non-El Nino-related. Only when the season is designated El Nino-related does it appear likely that seasonal rates might be slightly diminished.

Wilson, Robert M.↗

NASA NTRS2003

Seasonal Evolution and Variability Associated with the West African Monsoon System

In this study, we investigate the seasonal variations in surface rainfall and associated large-scale processes in the tropical eastern Atlantic and West African region. The 5-yr (1998-2002) high-quality TRMM rainfall, sea surface temperature (SST), water vapor and cloud liquid water observations are applied along with the NCEP/NCAR reanalysis wind components and a 3-yr (2000-2002) Quickscat satellite-observed surface wind product. Major mean rainfall over West Africa tends to be concentrated in two regions and is observed in two different seasons, manifesting an abrupt shift of the mean rainfall zone during June-July. (i) Near the Gulf of Guinea (about 5 degN), intense convection and rainfall are seen during April-June and roughly follow the seasonality of SST in the tropical eastern Atlantic. (ii) Along the latitudes of about 10 deg. N over the interior West African continent, a second intense rain belt begins to develop from July and remains there during the later summer season. This belt co-exists with a northwardmoved African Easterly Jet (AEJ) and its accompanying horizonal and vertical shear zones, the appearance and intensification of an upper tropospheric Tropical Easterly Jet (TEJ), and a strong low-level westerly flow. Westward-propagating wave signals [ i e . , African easterly waves (AEWs)] dominate the synoptic-scale variability during July-September, in contrast to the evident eastward-propagating wave signals during May- June. The abrupt shift of mean rainfall zone thus turns out to be a combination of two different physical processes: (i) Evident seasonal cycles in the tropical eastern Atlantic ocean which modulate convection and rainfall in the Gulf of Guinea by means of SST thermal forcing and SST-related meridional gradient; (ii) The interaction among the AEJ, TEJ, low-level westerly flow, moist convection and AEWs during July-September which modulates rainfall variability in the interior West Africa, primarily within the ITCZ rain band. Evident seasonality in synoptic-scale wave signals is shown to be a good evidence for this seasonal evolution.

Gu, Guojun↗

NASA NTRS2003

Seasonal Variation and Ecosystem Dependence of Emission Factors for Selected Trace Gases and PM2.5 for Southern African Savanna Fires

In this paper we present the first early dry season (early June-early August) emission factor measurements for carbon dioxide (CO2), carbon monoxide (CO), methane (Ca), nonmethane hydrocarbons (NMHC), and particulates with a diameter less than 2.5 microns (pM2.5) for southern African grassland and woodland fires. Seasonal emission factors for grassland fires correlate linearly with the proportion of green grass, used as a surrogate for the fuel moisture content, and are higher for products of incomplete combustion in the early part of the dry season compared with later in the dry season. Models of emission factors for NMHC and PM(sub 2.5) versus modified combustion efficiency (MCE) are statistically different in grassland compared with woodland ecosystems. We compare predictions based on the integration of emissions factors from this study, from the southern African Fire-Atmosphere Research Initiative 1992 (SAFARI-92), and from SAFARI-2000 with those based on the smaller set of ecosystem-specific emission factors to estimate the effects of using regional-average rather than ecosystem-specific emission factors. We also test the validity of using the SAFARI-92 models for emission factors versus MCE to predict the early dry season emission factors measured in this study. The comparison indicates that the largest discrepancies occur at the low end (0.907) and high end (0.972) of MCE values measured in this study. Finally, we combine our models of MCE versus proportion of green grass for grassland fires with emission factors versus MCE for selected oxygenated volatile organic compounds measured in the SAFARI-2000 campaign to derive the first seasonal emission factors for these compounds. The results of this study demonstrate that seasonal variations in savanna fire emissions are important and should be considered in modeling emissions at regional to continental scales.

Korontzi, S.↗