Interannual variability and trends in tropical ozone derived from SAGE II satellite data and SHADOZ ozonesondes

2011 ◽  
Vol 116 (D7) ◽  
Author(s):  
William J. Randel ◽  
Anne M. Thompson
Keyword(s):  
Interannual Variability ◽  
Satellite Data

scholarly journals Interannual variability of global biomass burning emissions from 1997 to 2004

2006 ◽  
Vol 6 (2) ◽  
pp. 3175-3226 ◽  
Author(s):  
G. R. van der Werf ◽  
J. T. Randerson ◽  
L. Giglio ◽  
G. J. Collatz ◽  
P. S. Kasibhatla ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Biomass Burning ◽  
Satellite Data ◽  
Net Primary Production ◽  
Terrestrial Ecosystems ◽  
Total Carbon ◽  
Burned Area ◽  
Biogeochemical Model ◽  
Fuel Wood ◽  
Fire Emissions

Abstract. Biomass burning represents an important source of atmospheric aerosols and greenhouse gases, yet little is known about its interannual variability or the underlying mechanisms regulating this variability at continental to global scales. Here we investigated fire emissions during the 8 year period from 1997 to 2004 using satellite data and the CASA biogeochemical model. Burned area from 2001–2004 was derived using newly available active fire and 500 m burned area datasets from MODIS following the approach described by Giglio et al. (2005). ATSR and VIRS satellite data were used to extend the burned area time series back in time through 1997. In our analysis we estimated fuel loads, including peatland fuels, and the net flux from terrestrial ecosystems as the balance between net primary production (NPP), heterotrophic respiration (Rh), and biomass burning, using time varying inputs of precipitation (PPT), temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation (fAPAR). For the 1997–2004 period, we found that on average approximately 58 Pg C year−1 was fixed by plants, and approximately 95% of this was returned back to the atmosphere via Rh. Another 4%, or 2.5 Pg C year−1 was emitted by biomass burning; the remainder consisted of losses from fuel wood collection and subsequent burning. At a global scale, burned area and total fire emissions were largely decoupled from year to year. Total carbon emissions tracked burning in forested areas (including deforestation fires in the tropics), whereas burned area was largely controlled by savanna fires that responded to different environmental and human factors. Biomass burning emissions showed large interannual variability with a range of more than 1 Pg C year−1, with a maximum in 1998 (3.2 Pg C year−1) and a minimum in 2000 (2.0 Pg C year−1).

scholarly journals Variability of Air–Sea Interactions over the Indian Ocean Derived from Satellite Observations

1998 ◽  
Vol 11 (8) ◽  
pp. 1859-1873 ◽  
Author(s):  
Catherine Gautier ◽  
Peter Peterson ◽  
Charles Jones
Keyword(s):  
Heat Flux ◽  
Indian Ocean ◽  
Interannual Variability ◽  
Satellite Data ◽  
Large Scale ◽  
Southwest Monsoon ◽  
Seasonal And Interannual Variability ◽  
The Indian Ocean ◽  
The Difference

Abstract Novel ways of monitoring the large-scale variability of the southwest monsoon in the Indian Ocean are presented using multispectral satellite datasets. The fields of sea surface temperature (SST), surface latent heat flux (LHF), net surface solar radiation (SW), precipitation (P), and SW − LHF over the Indian Ocean are analyzed to characterize the seasonal and interannual variability with special emphasis on the period 1988–90. It is shown that satellite data are able to make a significant contribution to the multiplatform strategy necessary to describe the large-scale spatial and temporal variability of air–sea interactions associated with the Indian Ocean Monsoon. The satellite data analyzed here has shown for the first time characteristics of the interannual variability of air–sea interactions over the entire Indian Ocean. Using monthly means of SST, LHF, SW, P, and the difference SW − LHF, the main features of the seasonal and interannual variability of air–sea interactions over the Indian Ocean are characterized. It is shown that the southwest monsoon strongly affects these interactions, inducing dramatic exchanges of heat between air and sea and large temporal variations of these exchanges over relatively small timescale (with regards to typical oceanic timescales). The analyses indicate an overall good agreement between satellite and in situ (ship) estimates, except in the southern Indian Ocean, where ship sampling is minimal, the disagreement can be large. In the latitudinal band of 10°N–15°S, differences in climatological in situ estimates of surface sensible heat flux and net longwave radiation has a larger influence on the net surface heat flux than the difference between satellite and in situ estimates of SW and LHF.

Global surface energy and water cycle variability 2001-2011 from satellite data

2020 ◽  
Author(s):  
Bo Dong ◽  
Keith Haines ◽  
Chris Thomas ◽  
Chunlei Liu ◽  
Richard Allan
Keyword(s):  
Surface Energy ◽  
Interannual Variability ◽  
Satellite Data ◽  
Water Budget ◽  
Water Cycle ◽  
Water Storage ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Water Budgets ◽  
Turbulent Heat Fluxes

<p>We derive internally consistent, monthly to interannual, energy and water budgets, with uncertainties, for all the main continents and ocean basins over 2001-2011 based principally on satellite data. An inverse model is used following the Thomas et al (2019) climatology study and the NASA energy and water cycle study (NEWS), L’Ecuyer et al. (2015), Rodell et al. (2015).<br>Input data include CERES and Cloud_CCI AATSR (radiation), FluxCOM (land turbulent heat fluxes), JOFURO3 (ocean turbulent heat fluxes), GPCP2.3 (Precipitation), GRACE (total water storage), ERA5 (atmospheric water storage), GRUNv1 (land runoff), and we compare these with alternative products to assess component uncertainties. The different components are then brought together and adjusted within respective uncertainties to achieve balanced energy and water budgets.<br>Preliminary results focus on seasonal and interannual variability over land. Seasonal modifications to the water budget over Eurasia and N America include a delay in spring runoff (and reduced evapotranspiration over Eurasia) as GRACE data indicates retention of water mass over land. Evapotranspiration adjustments to FluxCOM are strongly seasonal and also result in bringing the land seasonal energy budget closer to the DEEPC Liu et al (2015) results demonstrating the value of coupling the energy and water cycles.<br>Strong correlated interannual variability in African precipitation, runoff and GRACE derived water storage is found, and we assess the relative consistency of different data products, particularly for precipitation, where multiple datasets are available and uncertainties are large. Consistent African precipitation variability is found in the TAMSAT data, which further supports the water cycle change scheme around year 2006 over Africa. Clear ENSO signals are seen, particularly over South America in 2010 and Australia in 2010-11, with correlated variability in rainfall, runoff and water storage distributions. <br>Optimisation is sensitive to the uncertainty of each energy and water budget component expressed in their spatial and temporal error covariances.  We introduce spatial error covariance for turbulent heat fluxes between major ocean basins as well as temporal error covariances for all components expressing the expectation of time mean bias adjustments. The results show improved net surface energy flux pattern with larger heat loss over North Atlantic and Arctic Ocean and more heat uptake for other basins and an intensified water cycle, with increased precipitation, evapotranspiration and runoff and stronger ocean-land water transports. </p>

scholarly journals Assessment of the interannual variability and influence of the QBO and upwelling on tracer–tracer distributions of N<sub>2</sub>O and O<sub>3</sub> in the tropical lower stratosphere

2013 ◽  
Vol 13 (7) ◽  
pp. 3619-3641 ◽  
Author(s):  
F. Khosrawi ◽  
R. Müller ◽  
J. Urban ◽  
M. H. Proffitt ◽  
G. Stiller ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Satellite Data ◽  
General Circulation ◽  
Climate Model ◽  
Potential Temperature ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Satellite Measurements ◽  
Quasi Biennial Oscillation ◽  
Model Simulations

Abstract. A modified form of tracer–tracer correlations of N2O and O3 has been used as a tool for the evaluation of atmospheric photochemical models. Applying this method, monthly averages of N2O and O3 are derived for both hemispheres by partitioning the data into altitude (or potential temperature) bins and then averaging over a fixed interval of N2O. In a previous study, the method has been successfully applied to the evaluation of two chemical transport models (CTMs) and one chemistry–climate model (CCM) using a 1 yr climatology derived from the Odin Sub-Millimetre Radiometer (Odin/SMR). However, the applicability of a 1 yr climatology of monthly averages of N2O and O3 has been questioned due to the inability of some CCMs to simulate a specific year for the evaluation of CCMs. In this study, satellite measurements from Odin/SMR, the Aura Microwave Limb Sounder (Aura/MLS), the Michelson Interferometer for Passive Atmospheric Sounding on ENVISAT (ENVISAT/MIPAS), and the Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere (CRISTA-1 and CRISTA-2) as well as model simulations from the Whole Atmosphere Community Climate Model (WACCM) are considered. By using seven to eight years of satellite measurements derived between 2003 and 2010 from Odin/SMR, Aura/MLS, ENVISAT/MIPAS and six years of model simulations from WACCM, the interannual variability of lower stratospheric monthly averages of N2O and O3 is assessed. It is shown that the interannual variability of the monthly averages of N2O and O3 is low, and thus can be easily distinguished from model deficiencies. Furthermore, it is investigated why large differences are found between Odin/SMR observations and model simulations from the Karlsruhe Simulation Model of the Middle Atmosphere (KASIMA) and the atmospheric general circulation model ECHAM5/Messy1 for the Northern and Southern Hemisphere tropics (0° to 30° N and 0° to −30° S, respectively). The differences between model simulations and observations are most likely caused by an underestimation of the quasi-biennial oscillation and tropical upwelling by the models as well as due to biases and/or instrument noise from the satellite instruments. A realistic consideration of the QBO in the model reduces the differences between model simulation and observations significantly. Finally, an intercomparison between Odin/SMR, Aura/MLS, ENVISAT/MIPAS and WACCM was performed. The comparison shows that these data sets are generally in good agreement, although some known biases of the data sets are clearly visible in the monthly averages. Nevertheless, the differences caused by the uncertainties of the satellite data sets are sufficiently small and can be clearly distinguished from model deficiencies. Thus, the method applied in this study is not only a valuable tool for model evaluation, but also for satellite data intercomparisons.

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