Variability of CO2 From Satellite Retrievals and Model Simulations

Slides:



Advertisements
Similar presentations
GHG Verification & the Carbon Cycle 28 September 2010 JH Butler, NOAA CAS Management Group Meeting Page 1 Global Monitoring, Carbon Cycle Science, and.
Advertisements

Climate change in the Antarctic. Turner et al, Significant warming of the Antarctic Winter Troposphere. Science, vol 311, pp Radiosonde.
A direct carbon budgeting approach to infer carbon sources and sinks from the NOAA/ESRL Aircraft Network Colm Sweeney 1, Cyril Crevoisier 2, Wouter Peters.
Inter-comparison of retrieved CO 2 from TCCON, combining TCCON and TES to the overpass flight data Le Kuai 1, John Worden 1, Susan Kulawik 1, Kevin Bowman.
Variability in Ozone Profiles at TexAQS within the Context of an US Ozone Climatology Mohammed Ayoub 1, Mike Newchurch 1 2, Brian Vasel 3 Bryan Johnson.
The influence of extra-tropical, atmospheric zonal wave three on the regional variation of Antarctic sea ice Marilyn Raphael UCLA Department of Geography.
Interpreting MLS Observations of the Variabilities of Tropical Upper Tropospheric O 3 and CO Chenxia Cai, Qinbin Li, Nathaniel Livesey and Jonathan Jiang.
Vertically constrained CO 2 retrievals from TCCON Measurements Vertically constrained CO 2 retrievals from TCCON Measurements Le Kuai 1, Brain Connor 2,
Channel Selection for CO 2 Retrieval Using Near Infrared Measurements EGU 2009 Le Kuai 1, Vijay Natraj 1, Run-Lie Shia 1, Susan Kulawik 2, Kevin Bowman.
Assimilation of TES O 3 data in GEOS-Chem Mark Parrington, Dylan Jones, Dave MacKenzie University of Toronto Kevin Bowman Jet Propulsion Laboratory California.
Climate Signal Detection from Multiple Satellite Measurements Yibo Jiang, Hartmut H. Aumann Jet Propulsion Laboratory, Californian Institute of Technology,
Figure 3. The MJO-related vertical structures of MACC CO. CO are averaged between 15ºN – 15ºS. Rainfall is averaged between 5ºN – 5ºS. ABSTRACT. We report.
Distribution of H 2 O and SO 2 in the atmosphere of Venus Yung Y. 1, Zhang X. 1, Liang M.-C. 2 and Parkinson C. 3 1 California Institute of Technology.
CO 2 in the middle troposphere Chang-Yu Ting 1, Mao-Chang Liang 1, Xun Jiang 2, and Yuk L. Yung 3 ¤ Abstract Measurements of CO 2 in the middle troposphere.
Inter-comparison of retrieved CO 2 from TCCON, combining TCCON and TES to the overpass flight data Le Kuai 1, John Worden 1, Susan Kulawik 1, Edward Olsen.
Inter-comparison of retrieved CO 2 from TCCON, combining TCCON and TES to the overpass flight data Le Kuai 1, John Worden 1, Susan Kulawik 1, Kevin Bowman.
By studying the case with QBO signal only, the model reproduces the previous observation that QBO signal of column ozone at equator is anti-correlated.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Atmospheric Infrared Sounder.
1 Trends and Anomalies in Southern Hemisphere OH Inferred from 12 Years of 14 CO Data Martin Manning, Dave Lowe, Rowena Moss, Gordon Brailsford National.
A Channel Selection Method for CO 2 Retrieval Using Information Content Analysis Le Kuai 1, Vijay Natraj 1, Run-Lie Shia 1, Charles Miller 2, Yuk Yung.
Influence of the Brewer-Dobson Circulation on the Middle/Upper Tropospheric O 3 Abstract Lower Stratosphere Observations Models
(a)(b)(c) Simulation of upper troposphere CO 2 from two-dimensional and three-dimensional models Xun Jiang 1, Runlie Shia 2, Qinbin Li 1, Moustafa T Chahine.
Rising Temperatures. Various Temperature Reconstructions from
TEMPLATE DESIGN © Total Amount Altitude Optical Depth Longwave High Clouds Shortwave High Clouds Shortwave Low Clouds.
Sub-Saharan rainfall variability as simulated by the ARPEGE AGCM, associated teleconnection mechanisms and future changes. Global Change and Climate modelling.
High Climate Sensitivity Suggested by Satellite Observations: the Role of Circulation and Clouds AGU Fall Meeting, San Francisco, CA, 9-13 December, 2013.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Atmospheric Infrared Sounder.
Le Kuai 1, John Worden 2, Elliott Campbell 3, Susan S. Kulawik 4, Meemong Lee 2, Stephen A. Montzka 5, Joe Berry 6, Ian Baker 7, Scott Denning 7, Randy.
El Niño-Southern Oscillation in Tropical Column Ozone and A 3.5-year signal in Mid-Latitude Column Ozone Jingqian Wang, 1* Steven Pawson, 2 Baijun Tian,
Response of Middle Atmospheric Hydroxyl Radical to the 27-day Solar Forcing King-Fai Li 1, Qiong Zhang 2, Shuhui Wang 3, Yuk L. Yung 2, and Stanley P.
MIR OZONE ISSUES Horizontal (STE) and vertical transport (long life time in UTLS) Photochemical production by precursors (biomass burning, lightning,..)
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 1 Atmospheric Infrared.
Assimilating tropospheric ozone data from TES Mark Parrington, Dylan Jones University of Toronto Kevin Bowman Jet Propulsion Laboratory California Institute.
The effects of solar variability on the Earth’s climate Joanna D. Haigh 2010/03/09 Pei-Yu Chueh.
CO 2 Diurnal Profiling Using Simulated Multispectral Geostationary Measurements Vijay Natraj, Damien Lafont, John Worden, Annmarie Eldering Jet Propulsion.
Investigation of Atmospheric Recycling Rate from Observation and Model James Trammell 1, Xun Jiang 1, Liming Li 2, Maochang Liang 3, Jing Zhou 4, and Yuk.
Figure (a-c). Latitude-height distribution of monthly mean ozone flux for the months of (a) January, (b) April and (c) July averaged over years 2000 to.
Cambiamento attuale: Biogeochimica CLIMATOLOGIA Prof. Carlo Bisci.
Carbon dioxide from TES Susan Kulawik F. W. Irion Dylan Jones Ray Nassar Kevin Bowman Thanks to Chip Miller, Mark Shephard, Vivienne Payne S. Kulawik –
AGU2012-GC31A963: Model Estimates of Pan-Arctic Lake and Wetland Methane Emissions X.Chen 1, T.J.Bohn 1, M. Glagolev 2, S.Maksyutov 3, and D. P. Lettenmaier.
Retrieval of Methane Distributions from IASI
Camp et al. (2003) illustrated that two leading modes of tropical total ozone variability exhibit structrures of the QBO and the solar cycle. Figure (1)
Influence of Tropical Biennial Oscillation on Carbon Dioxide Jingqian Wang 1, Xun Jiang 1, Moustafa T. Chahine 2, Edward T. Olsen 2, Luke L. Chen 2, Maochang.
 We also investigated the vertical cross section of the vertical pressure velocity (dP/dt) across 70°W to 10°E averaged over 20°S-5°S from December to.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Tropospheric Emission Spectrometer Studying.
The Influence of loss saturation effects on the assessment of polar ozone changes Derek M. Cunnold 1, Eun-Su Yang 1, Ross J. Salawitch 2, and Michael J.
Climatic implications of changes in O 3 Loretta J. Mickley, Daniel J. Jacob Harvard University David Rind Goddard Institute for Space Studies How well.
Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.
This report presents analysis of CO measurements from satellites since 2000 until now. The main focus of the study is a comparison of different sensors.
(a)(b)(c) Simulation of upper troposphere CO 2 from two-dimensional and three-dimensional models Xun Jiang 1, Runlie Shia 2, Qinbin Li 1, Moustafa T Chahine.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Atmospheric Infrared Sounder.
Convective Transport of Carbon Monoxide: An intercomparison of remote sensing observations and cloud-modeling simulations 1. Introduction The pollution.
Variability of CO 2 From Satellite Retrievals and Model Simulations Xun Jiang 1, David Crisp 2, Edward T. Olsen 2, Susan S. Kulawik 2, Charles E. Miller.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS SURFACE PRESSURE MEASUREMENTS FROM THE ORBITING CARBON OBSERVATORY-2.
Polarization Effects on Column CO 2 Retrievals from GOSAT Measurements Vijay Natraj 1, Hartmut Bösch 2, Robert J.D. Spurr 3, Yuk L. Yung 4 1 Jet Propulsion.
Potential of Observations from the Tropospheric Emission Spectrometer to Constrain Continental Sources of Carbon Monoxide D. B. A. Jones, P. I. Palmer,
Yuqiang Zhang1, Owen R, Cooper2,3, J. Jason West1
Amanda Maycock & Piers Forster
Institut für Meteorologie
Polarization Effects on Column CO2 Retrievals from Non-Nadir Satellite Measurements in the Short-Wave Infrared Vijay Natraj1, Hartmut Bösch2, Robert J.D.
Global hydrological forcing: current understanding
Energy accumulation and surface warming
Investigation of Atmospheric Recycling Rate from Observation and Model PI: Xun Jiang1; Co-I: Yuk L. Yung2 1 Department of Earth & Atmospheric Sciences,
Variability of CO2 From Satellite Retrievals and Model Simulations
Continental outflow of ozone pollution as determined by ozone-CO correlations from the TES satellite instrument Lin Zhang Daniel.
Polarization Effects on Column CO2 Retrievals from Non-Nadir Satellite Measurements in the Short-Wave Infrared Vijay Natraj1, Hartmut Bösch2, Robert J.D.
Prescribed forcings. Prescribed forcings. (Top) Volcanic forcing is indicated as global visible optical depth. (Middle) Solar forcing is obtained by scaling.
Off-line 3DVAR NOx emission constraints
Climatic implications of changes in O3
Presentation transcript:

Variability of CO2 From Satellite Retrievals and Model Simulations Xun Jiang1, David Crisp2, Edward T. Olsen2, Susan S. Kulawik2, Charles E. Miller2, Thomas S. Pagano2, and Yuk L. Yung3 1Department of Earth & Atmospheric Sciences, University of Houston, Houston, TX 77204; 2Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109; 3Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125 Abstract Satellite CO2 retrievals from the Greenhouse gases Observing SATellite (GOSAT), Atmospheric Infrared Sounder (AIRS), and Tropospheric Emission Spectrometer (TES) and in-situ measurements from the Earth System Research Laboratory (NOAA-ESRL) Surface CO2 and Total Carbon Column Observing Network (TCCON) are utilized to explore the CO2 variability at different altitudes. A multiple regression method is used to calculate the CO2 annual cycle and semiannual cycle amplitudes from different data sets. The CO2 annual cycle and semiannual cycle amplitudes for GOSAT XCO2 and TCCON XCO2 are consistent, but smaller than those seen in the NOAA-ESRL surface data. The CO2 annual and semiannual cycles are smallest in the AIRS mid-tropospheric CO2 compared with other data sets in the northern hemisphere. Similar regression analysis is applied to the Model for OZone And Related chemical Tracers-2 (MOZART-2) and CarbonTracker model CO2. The convolved model CO2 annual cycle and semiannual cycle amplitudes are similar to those from the satellite CO2 retrievals, although the model tends to underestimate the CO2 annual cycle amplitudes in the northern hemisphere mid-latitudes and underestimate the CO2 semi-annual cycle amplitudes in the high latitudes. AIRS mid-tropospheric CO2 data are also used to explore the variability of CO2 over the South Atlantic Ocean. It was found that the CO2 difference is ~1 ppm between the South Atlantic Ocean and South America during December to March. During December to March, there is sinking motion over the South Atlantic Ocean. The sinking air brings high altitude low concentration of CO2 to the mid-troposphere. Meanwhile, air rises over South America, which brings surface high concentration of CO2 to the mid-troposphere over South America. As a result, the mid-tropospheric CO2 concentrations are lower over the South Atlantic Ocean than over South America during December to March. It is also found that the detrended AIRS mid-tropospheric CO2 difference correlates well with the inverted and detrended 400 hPa vertical pressure velocity difference between South Atlantic and South America. Results obtained from this study demonstrate the strong modulation of large-scale circulation on the mid-tropospheric CO2 and suggest that mid-tropospheric CO2 measurements can be used as an innovative observational constraint on the simulation of large-scale circulations in climate models. Figure 2: (a) Comparison of annual cycle amplitudes between AIRS mid-tropospheric CO2 and model convolved CO2 from MOZART (dotted line) and CarbonTracker (dash-dot line) (b) Comparison of semiannual cycle amplitudes between AIRS mid-tropospheric CO2 and model convolved CO2, (c) and (d) are the comparisons of annual and semiannual cycle amplitudes between TES mid-tropospheric CO2 and model convolved CO2. (e) and (f) are the comparisons of annual and semiannual cycle amplitudes between GOSAT XCO2 and model convolved CO2. (g) and (h) are the comparisons of annual and semiannual cycle amplitudes between NOAA-ESRL surface CO2 and model surface CO2. Units are ppm. Figure is from Jiang et al. [2014a]. Figure 4: (a) Vertical pressure velocity (dP/dt) averaged over 20S-5S from December to March in 2003-2010. Units are 10-2 Pa/s. Solid white contours refer to the sinking air. Dashed white contours refer to the rising air. (b) AIRS CO2 averaged over 20S-5S from December to March in 2003-2010. Units are ppm. Figure is from Jiang et al. [2014b]. Results for the annual cycle and semiannual cycle amplitudes from the model convolved CO2 are plotted against satellite and surface CO2 in Fig. 2. The model convolved CO2 annual cycle amplitudes are similar to those from satellite CO2 and NOAA-ESRL surface CO2. The values obtained by convolving the model CO2 by the GOSAT averaging kernel are larger than the values obtained by convolving the model CO2 by the AIRS CO2 averaging kernel, because the GOSAT XCO2 averaging kernel’s maximum is closer to the surface than that for the AIRS CO2 averaging kernel. Both models show CO2 semiannual cycles that are larger in the NH than SH. The CO2 semiannual cycle amplitude obtained by convolving the models with the GOSAT averaging kernel is about 0.5-2 ppm in the NH, which is similar to the measured GOSAT CO2 semiannual cycle shown in Fig. 2f. The amplitude of the CO2 semiannual cycle obtained by convolving the models with the AIRS averaging kernel is about 0.5-1 ppm in the NH, which is weaker than that from AIRS CO2 semiannual cycle in the high latitudes and need further exploration with in-situ CO2 profile data in the future. Figure 5: (a) Difference of the detrended AIRS mid-tropospheric CO2 between the South Atlantic Ocean (30W-10E; 20S-5S) and South America (70W-40W, 20S-5S) (black solid line) and difference of the inverted and detrended 400 hPa vertical pressure velocity (dP/dt) between the South Atlantic Ocean and the South America from reanalysis data and CMIP5 models. Different dashed color lines are from different reanalysis data and CMIP5 models. Bold red dashed line is the averaged vertical pressure velocity difference from all reanalysis data and model simulations. (b) Correlation coefficients between detrended CO2 difference and detrended and inverted vertical pressure velocity differences from reanalysis data and CMIP5 models. A 3-month running mean has been applied to all time series to remove the high frequency signals. Figure is from Jiang et al. [2014b]. CO2 Annual Cycle & Semiannual Cycle We investigated the temporal correlation between the South Atlantic Walker Circulation and the mid-tropospheric CO2 difference between the South Atlantic Ocean (30°W-10°E; 20°S-5°S) and the South America (70°W-40°W, 20°S-5°S) in Figure 5. As shown in Figure 5a, the detrended AIRS CO2 difference correlates well with the inverted and detrended vertical pressure velocity differences. The correlation coefficient between the detrended AIRS CO2 difference (black solid line) and the inverted and detrended mean vertical pressure velocity difference (red dashed line) is 0.66. The corresponding significance level is 9%. The correlation coefficients between the detrended AIRS CO2 difference and the inverted and detrended vertical pressure velocity differences derived from reanalysis datasets and CMIP5 model simulations differ from 0.55 to 0.72 (Figure 5b). The correlation coefficients are 0.67 for NCEP2, 0.64 for ERA-Interim, 0.55 for MERRA, between 0.57 and 0.72 for CMIP5 model simulations. Given the importance of large-scale circulation in driving global energy and water cycles, improving model simulations of large-scale circulation is critical to reducing the model spread in climate sensitivity estimates (Su et al. 2014). Since there are limited direct observations of vertical velocity, the mid-tropospheric CO2 can be utilized as an indirect constraint on model representation of large-scale circulation, for example, the vertical velocity of the South Atlantic Walker Cell. Figure 1: (a) Latitudinal distributions of CO2 annual cycle amplitudes. (b) Latitudinal distributions of CO2 semiannual cycle amplitudes. Blue lines are results from AIRS mid-tropospheric CO2. Green lines are results from GOSAT XCO2. Purple dots are results from NOAA-ESRL surface CO2. Orange triangles are results from TCCON XCO2. Error bars are the uncertainties of CO2 annual cycle and semiannual cycle amplitudes derived from the multiple regressions. Figure is from Jiang et al. [2014a]. Influence of South Atlantic Walker Circulation on CO2 Figure 3: AIRS mid-tropospheric CO2 averaged from December to March in 2003-2010. Units for CO2 are ppm. Color represents AIRS mid-tropospheric CO2. White contours are the NCEP2 400 hPa vertical pressure velocity (dP/dt). Solid white contours refer to the sinking air. Dashed white contours refer to the rising air. Figure is from Jiang et al. [2014b]. To understand the influence of the large-scale circulation on the mid-tropospheric CO2, we examined the AIRS mid-tropospheric CO2 distributions from December to March averaged over 2003-2010. NCEP2 400 hPa vertical pressure velocity (dP/dt) calculated in the same time period was overlain on the AIRS mid-tropospheric CO2 in Figure 3. Solid (dashed) white contours refer to the sinking (rising) air. Mid-tropospheric CO2 concentrations are the lowest over the South Atlantic Ocean, coincident with sinking air as shown by the white solid contours in Figure 3. Sinking air can bring low concentrations of CO2 from high altitude to the mid-troposphere, leading to low concentrations of mid-tropospheric CO2 over the Southern Atlantic Ocean. The rising air over South America brings high concentration of CO2 from the surface to the mid-troposphere, leading to relatively high concentrations of mid-tropospheric CO2 over South America. As shown in Figure 3, mid-tropospheric CO2 concentrations over the South Atlantic Ocean are about 1 ppm lower than that over South America from December to March. We also investigated the vertical cross section of the vertical pressure velocity (dP/dt) across 70W to 10E averaged over 20S-5S from December to March in 2003-2010. As shown in Figure 4a, it is clear that air sinks over 35W-10E and rises over 70W-35W. As a result of vertical motions, AIRS mid-tropospheric CO2 concentrations are relatively low over 35W-10E and relatively high over 70W-35W (Figure 4b). The difference of mid-tropospheric CO2 between South Atlantic Ocean and South America areas is about 1 ppm from December to March. We have applied a multiple regression method to all data sets (e.g., GOSAT XCO2, AIRS mid-tropospheric CO2, TES mid-tropospheric CO2, TCCON XCO2, and NOAA/ESRL surface CO2). We regressed CO2 data to the trend, annual, and semi-annual oscillation. The amplitudes for the CO2 annual cycle and the CO2 semiannual cycle are plotted in Figs. 1a and 1b, respectively. The CO2 annual cycle amplitudes are ~5-10 ppm for the NOAA-ESRL surface CO2 in the NH, which is almost a factor of two larger than those derived from the satellite CO2 retrievals. The annual cycle amplitudes of the GOSAT XCO2 are consistent with those from TCCON XCO2. For these two XCO2 data sets, the NH (SH) annual cycle amplitudes are about 2-3 ppm (0.5-1 ppm). TES CO2 annual cycle amplitudes are similar to GOSAT XCO2 in the NH. The NH CO2 annual cycle amplitude is smallest in the AIRS mid-tropospheric CO2. Since the CO2 annual cycle amplitudes are small in the SH, the differences of CO2 annual cycle amplitudes between different satellite CO2 retrievals are correspondingly small. Amplitudes for CO2 semiannual cycle are shown in Fig. 1b. The CO2 semiannual signal is largest at the surface, for the source for the semiannual signal in CO2 is mostly related to the CO2 exchange between biosphere and atmosphere at the surface [Jiang et al., 2012]. The CO2 semiannual signals are consistent between the GOSAT and TCCON CO2, which are smaller than that from the surface NOAA-ESRL CO2. The semiannual signal is smaller in the AIRS mid-tropospheric CO2 than GOSAT CO2 and TES CO2. Conclusions CO2 annual cycle and semiannual cycle amplitudes decrease with altitudes. Model convolved CO2 annual cycle and semiannual cycle amplitudes are similar to those from the satellite CO2 retrievals. Low concentrations of CO2 are seen over the Southern Atlantic Ocean, which is related to the sinking branch in the Atlantic Walker Circulation. AIRS mid-tropospheric CO2 difference correlates well with the inverted and detrended 400 hPa vertical pressure velocity difference between South Atlantic and South America. Satellite CO2 retrievals can be used as an innovative observational constraint on the simulation of large-scale circulation in climate models. References: Jiang, X., D. Crisp, E. T. Olsen, S. S. Kulawik, C. E. Miller, T. S. Pagano, M. Liang, and Y. L. Yung, (2014a), CO2 annual and semiannual cycles from multiple satellite retrievals and models, Submitted to ESS. Jiang, X., E. T. Olsen, T. S. Pagano, H. Su, and Y. L. Yung, (2014b), Modulation of mid-tropospheric CO2 by the South Atlantic Walker Circulation, Submitted to JAS. A41H-3164