Numerical Modelling of Dust Events for DODO and the Development of a New Dust Scheme in the UM Duncan Ackerley Ellie Highwood Mark Harrison Claire McConnell.

Slides:



Advertisements
Similar presentations
Dust-Climate Interactions in HadGEM2 Stephanie Woodward Met Office Hadley Centre
Advertisements

Slide 1 Dust Modelling Workshop, 26 Feb08 Dust Modelling Workshop, Reading U., 26 February 2008 Modelling dust aerosols for the ECMWF IFS J.-J. Morcrette,
Claire McConnell A new Saharan dust source activation frequency map derived from MSG-SEVIRI IR-channels.
Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.
4th Training Course on WMO SDS-WAS products: (satellite and ground observation and modelling of atmospheric dust) November 2014, Casablanca, Morocco.
N emissions and the changing landscape of air quality Rob Pinder US EPA Office of Research and Development Atmospheric Modeling & Analysis Division.
Development of a Simulated Synthetic Natural Color ABI Product for GOES-R AQPG Hai Zhang UMBC 1/12/2012 GOES-R AQPG workshop.
GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr.
Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009 Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009.
 Similar picture from MODIS and MISR aerosol optical depth (AOD)  Both biomass and dust emissions in the Sahel during the winter season  Emissions.
Constraining aerosol sources using MODIS backscattered radiances Easan Drury - G2
TEMPLATE DESIGN © North African Dust Export: A Global 3-D Model Analysis Using MODIS, MISR, CALIPSO, and AERONET Observations.
A global 3-D model analysis using MODIS, MISR, CALIPSO, and AERONET observations David A. Ridley, Colette L. Heald We gratefully acknowledge the MODIS.
Implementation of Sulfate and Sea-Salt Aerosol Microphysics in GEOS-Chem Hi everyone. My name is Win Trivitayanurak… I’m a PhD student working with Peter.
Using satellite observations to investigate natural aerosol loading Colette L. Heald David A. Ridley, Kateryna Lapina EGU April 5, 2011.
Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore.
30 years of African dust: From emission to deposition Using GEOS-Chem and MERRA to determine the causes of variability and trends David A. Ridley, Colette.
The July 2003 dust storm over Italy A strong Saharan dust outbreak occurred from 15 to 19 July 2003 over Italy. The figures below show the SeaWIFS image.
Aerosol Microphysics: Plans for GEOS-CHEM
© Crown copyright Met Office Met Office dust forecasting Using the Met Office Unified Model™ David Walters: Manager Global Atmospheric Model Development,
(Impacts are Felt on Scales from Local to Global) Aerosols Link Climate, Air Quality, and Health: Dirtier Air and a Dimmer Sun Emissions Impacts == 
Radiative Properties of Eastern Pacific Stratocumulus Clouds Zack Pecenak Evan Greer Changfu Li.
Operational assimilation of dust optical depth Bruce Ingleby, Yaswant Pradhan and Malcolm Brooks © Crown copyright 08/2013 Met Office and the Met Office.
In Situ and Remote Sensing Characterization of Spectral Absorption by Black Carbon and other Aerosols J. Vanderlei Martins, Paulo Artaxo, Yoram Kaufman,
Understanding the long-term variability of African dust as recorded in surface concentrations and TOMS observations Isabelle Chiapello (LOA, Lille, France)
High resolution simulation of August 1 AMMA case: impact of soil moisture initial state on the PBL dynamics and comparison with observations. S. Bastin.
Rick Saylor 1, Barry Baker 1, Pius Lee 2, Daniel Tong 2,3, Li Pan 2 and Youhua Tang 2 1 National Oceanic and Atmospheric Administration Air Resources Laboratory.
Aerosol Optical Depth during the Northern CA Fires of 2008 In situ aerosol light scattering and absorption measurements in Reno Nevada, 2008, indicated.
DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.
Influence of the Asian Dust to the Air Quality in US During the spring season, the desert regions in Mongolia and China, especially Gobi desert in Northwest.
The effect of pyro-convective fires on the global troposphere: comparison of TOMCAT modelled fields with observations from ICARTT Sarah Monks Outline:
Representation of Sea Salt Aerosol in CAM coupled with a Sectional Aerosol Microphysical Model CARMA Tianyi Fan, Owen Brian Toon LASP/ATOC, University.
1 Radiative impact of mineral dust on surface energy balance and PAR, implication for land-vegetation- atmosphere interactions Xin Xi Advisor: Irina N.
Dust Outflow and Deposition to the Ocean (DODO) A NERC SOLAS (UK) funded project P.I. Ellie Highwood: University of Reading CO-Is: Hugh Coe, Paul Williams,
Optical properties Satellite observation ? T,H 2 O… From dust microphysical properties to dust hyperspectral infrared remote sensing Clémence Pierangelo.
Overview of the tracer code in RegCM Tracers Aerosols ( droplets, smoke particles, dust, pollens, flying cats …) Gazeous phase, chemical species Evolution.
Bauru November 2004 Modelling interpretation of in situ H2O, CH4 and CO2 measured by  SDLA balloon borne instrument (SF2 and SF4 flights). N. Huret(1),G.
Modelling the radiative impact of aerosols from biomass burning during SAFARI-2000 Gunnar Myhre 1,2 Terje K. Berntsen 3,1 James M. Haywood 4 Jostein K.
NATURAL AND TRANSBOUNDARY POLLUTION INFLUENCES ON AEROSOL CONCENTRATIONS AND VISIBILITY DEGRADATION IN THE UNITED STATES Rokjin J. Park, Daniel J. Jacob,
Satellite Observations of Tropospheric Aerosols: More than Pretty Pictures Symposium in Honour of Jennifer Logan, Harvard University May 10, 2013 Colette.
Timothy Logan University of North Dakota Department of Atmospheric Science.
Page 1 Met Office © Crown copyright 2007 CAMM model performance assessed during DODO2 Steph Woodward – climate model dust scheme Glenn Greed – implementation.
Introduction 1. Advantages and difficulties related to the use of optical data 2. Aerosol retrieval and comparison methodology 3. Results of the comparison.
Rong-Ming Hu and Randall Martin Inspiring Minds. Retrieval of Aerosol Single Scattering Albedo (SSA)  Determined with radiative transfer calculation.
Ocean Surface Current Observations in PWS Carter Ohlmann Institute for Computational Earth System Science, University of California, Santa Barbara, CA.
Oceanic mixed layer heat budget in the Eastern Equatorial Atlantic using ARGO floats and PIRATA buoys M. Wade (1,2,3), G. Caniaux (1) and Y. du Penhoat.
Page 1© Crown copyright 2006 Modelled & Observed Atmospheric Radiation Balance during the West African Dry Season. Sean Milton, Glenn Greed, Malcolm Brooks,
One float case study The Argo float ( ) floating in the middle region of Indian Ocean was chosen for this study. In Figure 5, the MLD (red line),
How accurately we can infer isoprene emissions from HCHO column measurements made from space depends mainly on the retrieval errors and uncertainties in.
NGAC verification NGAC verification is comparing NGAC forecast (current AOT only) with observations from ground-based and satellite measurements and with.
Dust aerosols in NU-WRF – background and current status Mian Chin, Dongchul Kim, Zhining Tao.
An Observationally-Constrained Global Dust Aerosol Optical Depth (AOD) DAVID A. RIDLEY 1, COLETTE L. HEALD 1, JASPER F. KOK 2, CHUN ZHAO 3 1. CIVIL AND.
DODO RESULTS: Campaign Averages & BAe-146 Nephelometer Findings Claire McConnell Ellie Highwood Acknowledgements: Paola Formenti, Met Office, FAAM.
Chemical Data Assimilation: Aerosols - Data Sources, availability and needs Raymond Hoff Physics Department/JCET UMBC.
AEROCOM AODs are systematically smaller than MODIS, with slightly larger/smaller differences in winter/summer. Aerosol optical properties are difficult.
WORKSHOP ON CLIMATE CHANGE AND AIR QUALITY : part I: Intercontinental transport and climatic effects of pollutants OBJECTIVE: Define a near-term (-2003)
Assimilation of Satellite Derived Aerosol Optical Depth Udaysankar Nair 1, Sundar A. Christopher 1,2 1 Earth System Science Center, University of Alabama.
What drives the observed variability and decadal trends in North African dust export? David A. Ridley, Colette L. Heald Dept. Civil & Environmental Engineering,
Estimation of the contribution of mineral dust to the total aerosol depth: Particular focus on Atlantic Ocean G. Myhre, A. Grini, T.K. Berntsen, T.F. Berglen,
Modeling the emission, transport, and optical properties of Asian dust storms using coupled CAM/CARMA model Lin Su and Owen B. Toon Laboratory for Atmospheric.
Forecasting smoke and dust using HYSPLIT. Experimental testing phase began March 28, 2006 Run daily at NCEP using the 6Z cycle to produce a 24- hr analysis.
Tropical Atlantic SST in coupled models; sensitivity to vertical mixing Wilco Hazeleger Rein Haarsma KNMI Oceanographic Research The Netherlands.
number Typical aerosol size distribution area volume
Observational Error Estimation of FORMOSAT-3/COSMIC GPS Radio Occultation Data SHU-YA CHEN AND CHING-YUANG HUANG Department of Atmospheric Sciences, National.
Environmental Physics Laboratory, Institute of Physics Belgrade
Atmospheric modelling of the Laki eruption
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.
Contribution from Natural Sources of Aerosol Particles to PM in Canada
Maarten van Loon and Leonor Tarrasón (met.no/EMEP)
Presentation transcript:

Numerical Modelling of Dust Events for DODO and the Development of a New Dust Scheme in the UM Duncan Ackerley Ellie Highwood Mark Harrison Claire McConnell

Project Aims To develop the UM dust scheme further for real time and climate forecasting. To develop the UM dust scheme further for real time and climate forecasting. Provide constraints to the model using data from the DODO flight campaign (Claire). Provide constraints to the model using data from the DODO flight campaign (Claire). Use the dust scheme to quantify the seasonal dust deposition footprint to the Atlantic Ocean from the Sahara Desert. Use the dust scheme to quantify the seasonal dust deposition footprint to the Atlantic Ocean from the Sahara Desert.

Current Dust Modelling The current dust scheme has been produced by Stephanie Woodward. The current dust scheme has been produced by Stephanie Woodward. The scheme we are currently using divides the dust mass concentration into six size bins (although there is a version with nine). The scheme we are currently using divides the dust mass concentration into six size bins (although there is a version with nine). The representative radii for the bins range from – 17.8 μm The representative radii for the bins range from – 17.8 μm The threshold friction velocity is calculated for each individual size bin (depending on surface characteristics) such that emissions may occur from e.g. bins 3-6 but not bins 1 and 2. The threshold friction velocity is calculated for each individual size bin (depending on surface characteristics) such that emissions may occur from e.g. bins 3-6 but not bins 1 and 2. The dust is advected throughout the chosen domain (global in this case) by the model winds. The dust is advected throughout the chosen domain (global in this case) by the model winds.

The DEAD scheme Another dust uplift scheme has been incorporated into the UM as produced by Zender et al (2003). Another dust uplift scheme has been incorporated into the UM as produced by Zender et al (2003). Scheme is known as the Dust Entrainment And Deposition (DEAD) scheme and has been used by other institutions. Scheme is known as the Dust Entrainment And Deposition (DEAD) scheme and has been used by other institutions. The DEAD scheme has two major differences from the original scheme: The DEAD scheme has two major differences from the original scheme: –Once the threshold friction velocity is reached particles are emitted from all bins. –The scheme requires a preferential source term (based on the surface reflectivity) to constrain the dust emissions.

Preferential Source Term Squared and Linear source term fields used with the DEAD scheme. Squared and Linear source term fields used with the DEAD scheme. Both are based on satellite measurements of surface reflectivity. Both are based on satellite measurements of surface reflectivity. The squared term is used in the model as it provides a stronger gradient between strong and weak sources. The squared term is used in the model as it provides a stronger gradient between strong and weak sources. Linear Source Term Squared Source Term

DODO Winter Case The next set of case studies under investigation are from February 2006 over West Africa and the Atlantic Ocean. The next set of case studies under investigation are from February 2006 over West Africa and the Atlantic Ocean. The flights B173, B174 and B175 occurred on the 14 th, 15 th and 16 th February Model initialised with zero dust close to the 14 th February The flights B173, B174 and B175 occurred on the 14 th, 15 th and 16 th February Model initialised with zero dust close to the 14 th February Information about the size distribution and number concentration are being used to compare with output from DEAD and the Woodward schemes. Information about the size distribution and number concentration are being used to compare with output from DEAD and the Woodward schemes. The current focus is on the vertical dust number concentration and the dust size distribution. The current focus is on the vertical dust number concentration and the dust size distribution.

Initial Problems Dust number concentrations for the DODO observations (colours). Dust number concentrations for the DODO observations (colours). Dust number concentrations from the both model schemes are in black. Dust number concentrations from the both model schemes are in black. Further analysis shows far too much dust in Bin 1. Further analysis shows far too much dust in Bin 1. Over estimation also occurs in the summer case too. Over estimation also occurs in the summer case too.

Simplifying the model The model calculates the horizontal dust transport as a function of several variables such as friction velocity. The model calculates the horizontal dust transport as a function of several variables such as friction velocity. Model also uses the relative mass of dust in each size division on all land points derived from observations. Model also uses the relative mass of dust in each size division on all land points derived from observations. Latest version of the DEAD scheme fixes these relative masses over all land points. Latest version of the DEAD scheme fixes these relative masses over all land points.

New Profile Data Dust concentrations from observations are coloured. Dust concentrations from observations are coloured. Old model versions in dark lines. Old model versions in dark lines. New version given by red dashed lines. New version given by red dashed lines. Better agreement with observations. Better agreement with observations.

Size Distribution 1/N dN/dR 1/N dN/dR Observations in blue. Observations in blue. Original DEAD scheme plus Woodward scheme in Res/Black Original DEAD scheme plus Woodward scheme in Res/Black Output from latest version of DEAD in green. Output from latest version of DEAD in green. Again, much better agreement with observations. Again, much better agreement with observations.

Aerosol Optical Depth (AOD) The next phase of the comparison to observations is to look at the AOD. The next phase of the comparison to observations is to look at the AOD. Plot shows the AODs around midday on 14 th, 15 th and 16 th Feb Plot shows the AODs around midday on 14 th, 15 th and 16 th Feb Some slight differences. Some slight differences. The aim is to compare the AODs in these plots with those taken by Claire in DODO1. The aim is to compare the AODs in these plots with those taken by Claire in DODO1.

Conclusions The number concentration of small dust particles in the DEAD and Woodward schemes were initially far too high. The number concentration of small dust particles in the DEAD and Woodward schemes were initially far too high. Fixing the relative mass contribution of dust in each bin has reduced the number of small particles. Fixing the relative mass contribution of dust in each bin has reduced the number of small particles. Better agreement between model dust profiles and size distributions and those observed in DODO1 flight campaigns. Better agreement between model dust profiles and size distributions and those observed in DODO1 flight campaigns. Simplifying the model seems ho have helped make the output more realistic. Simplifying the model seems ho have helped make the output more realistic.

Future Work Compare the AODs measured on the flight campaign and from AERONET to the model output. Compare the AODs measured on the flight campaign and from AERONET to the model output. Begin to analyse the output for the summer DODO2 cases (August 2006) and compare with the flight campaign data. Begin to analyse the output for the summer DODO2 cases (August 2006) and compare with the flight campaign data.