Aerosol radiative effects from satellites Gareth Thomas Nicky Chalmers, Caroline Poulsen, Ellie Highwood, Don Grainger Gareth Thomas - NCEO/CEOI-ST Joint.

Slides:

Advertisements

Similar presentations

ADIENT is a NERC APPRAISE project made up of a consortium of leading UK experts in aerosol modelling and measurement with the overarching objective of.

Advertisements

Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.

Satellite Cloud and Aerosol climate records for the ESA Climate Change Initiative (CCI) Caroline Poulsen, Gareth Thomas, Richard Siddans, Don Grainger,

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.

Rutherford Appleton Laboratory 5th ADIENT Meeting 2 nd April 2009, Manchester University WP4.3.1 Comparisons of model simulations with global radiance.

Climate Forcing and Physical Climate Responses Theory of Climate Climate Change (continued)

Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,

Using observations to reduce uncertainties in climate model predictions Maryland Climate Change Workshop Prof. Daniel Kirk-Davidoff.

A 21 F A 21 F Parameterization of Aerosol and Cirrus Cloud Effects on Reflected Sunlight Spectra Measured From Space: Application of the.

Page 1 1 of 21, 28th Review of Atmospheric Transmission Models, 6/14/2006 A Two Orders of Scattering Approach to Account for Polarization in Near Infrared.

ARM Atmospheric Radiation Measurement Program. 2 Improve the performance of general circulation models (GCMs) used for climate research and prediction.

Page 1 1 of 20, EGU General Assembly, Apr 21, 2009 Vijay Natraj (Caltech), Hartmut Bösch (University of Leicester), Rob Spurr (RT Solutions), Yuk Yung.

Evaluation II – The Atmosphere Feedback describes the situation when output from (or information about the result of) an event or phenomenon in the past.

ESTEC July 2000 Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading.

Retrieval of thermal infrared cooling rates from EOS instruments Daniel Feldman Thursday IR meeting January 13, 2005.

Direct Radiative Effect of aerosols over clouds and clear skies determined using CALIPSO and the A-Train Robert Wood with Duli Chand, Tad Anderson, Bob.

Direct aerosol radiative forcing based on combined A-Train observations – challenges in deriving all-sky estimates Jens Redemann, Y. Shinozuka, M.Kacenelenbogen,

2010 CEOS Field Reflectance Intercomparisons Lessons Learned K. Thome 1, N. Fox 2 1 NASA/GSFC, 2 National Physical Laboratory.

Numerical diffusion in sectional aerosol modells Stefan Kinne, MPI-M, Hamburg DATA in global modeling aerosol climatologies & impact.

1. The MPI MAX-DOAS inversion scheme 2. Cloud classification 3. Results: Aerosol OD: Correlation with AERONET Surface extinction: Correlation with Nephelometer.

EARLINET and Satellites: Partners for Aerosol Observations Matthias Wiegner Universität München Meteorologisches Institut (Satellites: spaceborne passive.

Mixing State of Aerosols: Excess Atmospheric Absorption Paradox Shekhar Chandra Graduate Student, EAS Term Paper Presentation for EAS-6410.

Spatially Complete Global Surface Albedos Derived from MODIS Data

(Impacts are Felt on Scales from Local to Global) Aerosols Link Climate, Air Quality, and Health: Dirtier Air and a Dimmer Sun Emissions Impacts == 

MT Workshop October 2005 JUNE 2004 DECEMBER 2004 End of OCTOBER 2005 ? MAY 2002 ? Capabilities of multi-angle polarization cloud measurements from.

A. Bracher, L. N. Lamsal, M. Weber, J. P. Burrows University of Bremen, FB 1, Institute of Environmental Physics, P O Box , D Bremen, Germany.

Clear sky Net Surface Radiative Fluxes over Rugged Terrain from Satellite Measurements Tianxing Wang Guangjian Yan

JCSDA Summer Colloquium Erica Dolinar 4 August 2015.

Evaluation of climate models, Attribution of climate change IPCC Chpts 7,8 and 12. John F B Mitchell Hadley Centre How well do models simulate present.

ISCCP at its 30 th (New York, 22 – 25 April, 2013) Congratulations to the “Core Team” and to “Patient Outside-Supporters”: Our best wishes for a perspective.

Definition and assessment of a regional Mediterranean Sea ocean colour algorithm for surface chlorophyll Gianluca Volpe National Oceanography Centre, Southampton.

Direct aerosol radiative forcing based on combined A-Train observations and comparisons to IPCC-2007 results Jens Redemann, Y. Shinozuka, M. Vaughan, P.

Synthesis NOAA Webinar Chris Fairall Yuqing Wang Simon de Szoeke X.P. Xie "Evaluation and Improvement of Climate GCM Air-Sea Interaction Physics: An EPIC/VOCALS.

Estimating the radiative impacts of aerosol using GERB and SEVIRI H. Brindley Imperial College.

The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.

TOPIC III THE GREENHOUSE EFFECT. SOLAR IRRADIANCE SPECTRA 1  m = 1000 nm = m Note: 1 W = 1 J s -1.

Satellite group MPI Mainz Investigating Global Long-term Data Sets of the Atmospheric H 2 O VCD and of Cloud Properties.

Fog- and cloud-induced aerosol modification observed by the Aerosol Robotic Network (AERONET) Thomas F. Eck (Code 618 NASA GSFC) and Brent N. Holben (Code.

Aerosol_cci ECV. Aerosol_cci > Thomas Holzer-Popp > ESA Living Planet Symposium, Bergen, 1 July 2010 slide 2 Major improvements over precursor AOD datasets.

As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.

Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.

ESTIMATION OF SOLAR RADIATIVE IMPACT DUE TO BIOMASS BURNING OVER THE AFRICAN CONTINENT Y. Govaerts (1), G. Myhre (2), J. M. Haywood (3), T. K. Berntsen.

Uncertainty in aerosol retrievals: interaction with the community Adam Povey 1, Thomas Holzer-Popp 2, Gareth Thomas 3, Don Grainger 1, Gerrit de Leeuw.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Study Overview R.Siddans.

SCIAMACHY TOA Reflectance Correction Effects on Aerosol Optical Depth Retrieval W. Di Nicolantonio, A. Cacciari, S. Scarpanti, G. Ballista, E. Morisi,

Point Comparison in the Arctic (Barrow N, 156.6W ) Part I - Assessing Satellite (and surface) Capabilities for Determining Cloud Fraction, Cloud.

The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion.

Visible optical depth,  Optically thicker clouds correlate with colder tops Ship tracks Note, retrievals done on cloudy pixels which are spatially uniform.

Feb, 2007 JTH COP6bis/SBSTA Climate Change 2001: The Scientific Basis WGI contribution to IPCC Third Assessment Report Summary for Policymakers.

Direct aerosol radiative effects based on combined A-Train observations Jens Redemann, Y. Shinozuka, J. Livingston, M. Vaughan, P. Russell, M.Kacenelenbogen,

1 Xiong Liu Harvard-Smithsonian Center for Astrophysics K.V. Chance, C.E. Sioris, R.J.D. Spurr, T.P. Kurosu, R.V. Martin, M.J. Newchurch,

AEROCOM AODs are systematically smaller than MODIS, with slightly larger/smaller differences in winter/summer. Aerosol optical properties are difficult.

Interannual Variability and Decadal Change of Solar Reflectance Spectra Zhonghai Jin Costy Loukachine Bruce Wielicki (NASA Langley research Center / SSAI,

University of Oxford EUMETSAT Satellite Conference 2004 Aerosol Retrieval Algorithm for Meteosat Second Generation Sam Dean, Steven Marsh and Don Grainger.

Consistent Earth System Data Records for Climate Research: Focus on Shortwave and Longwave Radiative Fluxes Rachel T. Pinker, Yingtao Ma and Eric Nussbaumer.

MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.

NASA Langley Research Center / Atmospheric Sciences CERES Instantaneous Clear-sky and Monthly Averaged Radiance and Flux Product Overview David Young NASA.

What Are the Implications of Optical Closure Using Measurements from the Two Column Aerosol Project? J.D. Fast 1, L.K. Berg 1, E. Kassianov 1, D. Chand.

Fourth TEMPO Science Team Meeting

Quantifying rapid adjustments using radiative kernels

N. Bousserez, R. V. Martin, L. N. Lamsal, J. Mao, R. Cohen, and B. R

Vicarious calibration by liquid cloud target

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC Height-resolved aerosol R.Siddans.

Entrapment An important mechanism for shortwave 3D cloud radiative effects and its inclusion in the SPARTACUS radiative transfer model Robin Hogan, Mark.

Modelling the radiative impact of aerosols from biomass burning during SAFARI-2000 Gunnar Myhre, Terje K. Berntsen, James M. Haywood, Jostein K. Sundet,

Using dynamic aerosol optical properties from a chemical transport model (CTM) to retrieve aerosol optical depths from MODIS reflectances over land Fall.

ECV definitions Mapping of ECV product with OSCAR variables

Chris Sioris Kelly Chance

Studying the cloud radiative effect using a new, 35yr spanning dataset of cloud properties and radiative fluxes inferred from global satellite observations.

Presentation transcript:

Aerosol radiative effects from satellites Gareth Thomas Nicky Chalmers, Caroline Poulsen, Ellie Highwood, Don Grainger Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014

Overview Motivation: –why calculate aerosol radiative effect –connection to radiative forcing Methodology: –aggregation of satellite data into regional, monthly estimates –uncertainty and error analysis –radiative calculations Radiative forcing? What next See: G.E. Thomas et al., Atmos. Chem. Phys., 13: , 2013 Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014

Nomenclature Aerosol radiative effect Difference between the net downwelling broadband flux with and without aerosol at some atmospheric level: ΔR = (F ↓ – F ↑ ) aerosol – (F ↓ – F ↑ ) ”clean” Aerosol radiative forcing Follow the IPCC definition: change in radiative effect at top- of-atmosphere since 1750: R f = (F ↓ – F ↑ ) present – (F ↓ – F ↑ ) 1750 Note: aerosol radiative forcing is only takes account of changes in aerosol itself Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014

Why calculate aerosol radiative effect Climatically speaking aerosol is important for two reasons: 1.Scattering and absorption of solar radiation 2.Their influence on cloud properties Aerosol can result in either a significant cooling or warming at the surface Calculating the radiative effect is relatively straight forward (compared to forcing) Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014 IPCC AR5: Radiative forcing breakdown

Aggregation of satellite data Gareth Thomas - NCEO/CEOI-ST Joint Conference Regional evaluation versus AERONET – bias and scatter 2.Bias correction 3.Temporal/spatial averaging Level-2 satellite data: 10 km spatial resolution along satellite tracks Regional-monthly mean AOD fields: Averaged values for each region Bias corrected, with uncertainties GlobAEROSOL AATSR AOD: 2006

Uncertainties and error propagation A range of error terms need to be taken into account: Uncertainty in input data Error from spatially averaging AOD Error from temporally averaging AOD Uncertainty in aerosol radiative properties Uncertainty in assumed aerosol height distribution Error from spatially/temporally averaging surface albedo Error due to low spectral resolution Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014 Options for propagating uncertainty: 1.Brute force mapping of parameter uncertainty into radiative flux space – Monte Carlo simulation for instance. 2.Bayesian mapping of uncertainty into radiative flux – use gradient of radiative transfer model wrt to parameters. 3.Approximate by running radiative transfer at ±σ for each parameter.

Uncertainties and error propagation Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014 Random or systematic? Random errors add in quadrature Systematic errors add linearly → In this case assuming all errors are random gives more conservative uncertainty Relative or absolute? Are uncertainties best described as some fraction of the parameter of interest? Optical depth Spatial averaging Temporal averaging Radiative properties Albedo Spectral variability

Uncertainties and error propagation Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014 Spatial and/or temporal averaging, and aerosol radiative properties dominate uncertainty

Radiative effect Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014 GlobAEROSOL AATSR radiative effect: 2006

Radiative forcing? Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014 GlobAEROSOL AATSR radiative forcing: 2006

Next steps ESA Aerosol_cci project will provide 17 years (A)ATSR data –Much improved quality –Consistent calibration –3 independent algorithms Uncertainty propagation can be further improved Improvements to Radiative transfer Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014 Full AATSR record – global average AOD at AERONET sites (Plot: Peter North) 17 year record of global/region radiative effect with comprehensive uncertainties

Open issues Radiative forcing calculation remains problematic –Model/observation biases need to be solved –Deriving “anthropogenic fraction” from satellite observations is not straight forward either Uncertainty in, and variability of, aerosol properties will remain a limiting factor in error budget Coverage is not global –“Clear-sky” radiative effect only –The so called “twilight-zone” where observations are classified as neither cloud or aerosol Gareth Thomas - NCEO/CEOI-ST Joint Conference 2014