What controls the seasonal cycle of columnar methane observed by GOSAT over different regions in India?

Transcription

1 What controls the seasonal cycle of columnar methane observed by GOSAT over different regions in India? N. Chandra 1, S. Hayashida 1, T. Saeki 2 and P. K. Patra 2 1 Nara Woman s University, Japan 2 Department of Environmental Geochemical Cycle Research, JAMSTEC, Japan This is results from Atmospheric Methane from Agriculture in South Asia (AMASA)project, sponsored by MOE, Japan The 3 rd Third ACAM Workshop, June 5-9, 217 Jinan University, Guangzhou

2 Anthropogenic GHGs Why should we care for methane? Second most important driver of anthropogenic climate change. Resulting atmospheric drivers CO 2 CH 4 Halocarbons N 2 O CO IPCC, Radiative forcing relative to 175 (W m -2 ) Addresses climate change on time scales of decades. Sectorial emissions of CH 4 remain highly uncertain, particular from Asian region due to limited observations.

3 AMASA (Atmospheric Methane from Agriculture in South Asia) a project sponsored by the Environment Research and Technology Development : April 215-March 218 Leader: Sachiko Hayashida Goal 1: Improvement of Methane Emission Estimate from South Asia Goal 2: Development of an Emission Mitigation Proposal Mitigation scenarios from rice fields + A priori by proper water management and/or change in transplanting Emission estimate in regional scale posteriori 3

4 Satellite measurements of XCH 4 GOSAT (9 present) Columnar Methane (XCH4) SWIR -- XCH 4 -- Integrated measure of CH 4 with contributions from different vertical atmospheric layers. High XCH 4 High surface emissions? Turner et al. [215] ACTM can be used to investigate the role of transport and chemistry Emission information could not drive straightforwardly without separating the role of transport and chemistry in the XCH 4.

5 Aim of this study Understand the responsible factors for XCH 4 seasonal cycle over the Asian monsoon region. Data Used in present study: Period: Observations and model: GOSAT and JAMSTEC s ACTM Simulations : (Anthropogenic: EDGARV4.2; Wetl. & Rice: VISIT; Termite: GISS; Bio. Burn: GFED) Two different emission scenarios (AGS and CTL) are used to examine model sensitivity to change in the underlying fluxes in simulations of the total atmospheric column. AGS: All emission sectors in EDGAR42FT kept constant at the values for, except for the emissions from agricultural soils. CTL: EDGAR32/VISIT/GISS

6 XCH 4 (ppb) XCH 4 from GOSAT and ACTM over Indian region GOSAT ACTM_AGS ACTM_CTL N 3N 2N 1N AS AI WI WIGP CI SI a. Indian region EI EIGP NE India BOB N 6E 7E 8E 9E 1E Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan

7 XCH 4 (ppb) X p CH 4 (ppb) Role of vertical layers in XCH 4 mixing ratios UA (σ p =.2 ) UT (σ p =.4.2) Columnar Methane (XCH4) EIGP MT2 (σ p =.6.4) MT1 (σ p =.8.6) LT (σ p = 1..8) GOSAT ACTM_AGS ACTM_CTL UA UT MT2 MT1 LT GOSAT ACTM_AGS ACTM_CTL hpa hpa 4 hpa 6 hpa 8 hpa 1 hpa South India hpa Arid India EIGP South India Arid India Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan gch 4 m -2 mon -1

8 Contributions of vertical layers in XCH 4 mixing ratios hpa 1 Columnar Methane (XCH4) UA UT MT2 MT1 LT hpa 4 hpa 6 hpa 8 hpa 1 hpa Contribution from different layers (%) AI WIGP EIGP NEI EI CI WI SP SI AS BOB UA UT MT2 MT1 LT -2 Region Chandra et al., ACPD (217)

9 1 5 5 Source of higher CH 5 1N 4 in the upper troposphere -1 Convection N 1N 2N 3N 4N c. Summer (JAS); 83-93E Advection -1 N 1N 2N 3N 4N d. Autumn (OND); 83-93E N 1N 2N 3N 4N -1 1 CH 4 (ppb/day) Convective transport rate of CH 4 (ppb day -1 ) 83-93E N N1N 2N1N3N 4N2N 6E 7E 8E 3N 9E 1E 11E 4N 12E f. Spring (AMJ); g. Summer 83-93E (JAS); j. Spring 83-93E (AMJ); hpa 5 5 e. Winter (JFM); (JFM) 83-93E 4N i. Winter (JFM); hpa hpa 5 4N N 2N 5 5 1N 5 1N 1ms -1 1ms N N1N 2N1N3N 4N2N 6E 7E 8E 3N9E 1E 11E 4N 12E N g. 1N Summer h. 2N (JAS); Autumn (JJA) 83-93E 3N 4N(OND); 6E 7E k. Summer 83-93E 8E (JAS); 9E 1E hpa 11E 12E 5 f. Spring (AMJ); 83-93E j. Spring (AMJ); hpa 5 4N 5 4N N 2N 5 1N 5 1N 1ms -1 1ms N 1N 2N 3N 4N 6E 7E 8E 9E 1E 11E 12E N N h. 1N Autumn 2N (OND); 1N 83-93E 3N 4N2N 6E 7E l. Autumn 8E 3N(OND); 9E 1E hpa 11E 4N 12E g. Summer (JAS); 83-93E k. Summer (JAS); hpa 5 4N 5 4N CH 4 (ppb): 3N AGS 3N CH 4 (ppb) N 3N 3N 3N 2N 2N 1ms -1 Chandra et al., ACPD (217)

10 Conclusion Both convection and advection play significant role in transport and redistribution of CH 4 over the South Asian monsoon region. A direct link between surface emissions and higher levels of XCH 4 can not be established straightforwardly. Upper troposphere contribute strongly in the peak of XCH 4 most of the regions lying in the northern part of India. over

11 Thank You for your kind attention

12 V2 V2 V1 V1 V2 V1 g CH 4 kg -1 grain rice 7m MSRI V2 V1 CT V1 V2 Water depth (cm) Platform for gas sampling V2 V SRI V1 V2 R1 R2 R Soil 14 Site S: Mitigation Experiments using alternate wetting and drying (AWD) system of rice intensification (SRI) Wet Dry They succeeded to reduce CH4 emission from 16 rice paddies ~ 5% S R Gas sampling point V1 V V1 V m SRI MSRI CT AWD +SRI Conventional Transplanting Oo et al., paper in preparation

13 AMASA: 6 Sub-themes 1. Analysis of GOSAT and in-situ measurements of methane (NWU). 2. Methane measurements in South Asia (NIES). 3. Mitigation options of methane emissions (NIAES). 4. Methane flux measurements in South Asia (Chiba Univ.). 5. Continuous measurements of methane by a laser Instrument. 6. Inverse Analysis of Methane (JAMSTEC).

14 Pressure (hpa) N 1N 2N 3N 4N c. Summer (JAS); 83-93E 5 1N Source of higher CH 4 in the upper troposphere 1ms -1 1 N 1N 2N 3N 4N 1 b. Spring (AMJ); 83-93E 5 a. Winter (JFM); 83-93E N 1N 2N 3N 4N f. Spring (AMJ); 83-93E 5 e. Winter 83-93E (JFM); 83-93E Convective transport rate of CH 4 (ppb day -1 ) N N CH 4 (ppb): CH 4 (ppb) AGS N 2N 1 5 N 1N 2N 5 1N 3N 4N h. Autumn (OND); 1N 83-93E 1ms N 1N 2N 3N 4N -1 1ms d. Autumn (OND); 83-93E N 1N 2N 3N 4N 1N 1N 2N 3N 4N 6E 7E 8E 9E 1E 11E 12E 5N c. 1N 2N (JAS); 83-93E 3N 4N N g. 1N Summer 2N (JAS); 83-93E 3N 4N 6E 7E k. Summer 8E (JAS); 9E 1E hpa 11E 12E b. Summer(JJA) Spring (AMJ); 83-93E 5 f. Spring (AMJ); 83-93E j. Spring (AMJ); hpa 5 1 4N 5 5 4N N 1 1 3N 2N 2N 5 5 1N N 1ms -1 1ms N 4N 6E 4N N N h. 1N Autumn 2N (OND); 1N 83-93E 3N 4N2N 7E 8E 9E 3N 1E 11E 12E N d. 1N Autumn 2N (OND); 83-93E 3N 4N 6E 7E l. Autumn 8E (OND); 9E 1E hpa 11E 4N 12E c. Summer (JAS); 83-93E g. Summer (JAS); 83-93E k. Summer (JAS); hpa 5 5 4N 5 5 4N 1 1 Winter (JFM) 83-93E Convective transport rate of CH 4 (ppb/day) E 7E 8E 9E 1E 11E 12E j. Spring (AMJ); hpa 4N i. Winter (JFM); hpa hpa 4N Summer -- Higher CH 4 emissions as well as higher convective transport. Anticyclonic 5 winds --Traps 5 CH 5 4 -rich air mass and 1N further spread over the larger south 5 1N 1ms Asian region. -1 1ms N 1N 2N 3N 4N 1 1 N 1N 2N 3N 4N N 1N 2N 3N 4N N 1N 2N 3N 4N d. Autumn (OND); 83-93E h. Autumn (OND); 83-93E -1 3N 3N 2N 2N 6E 7E 8E 9E 1E 11E 12E 6E 7E 8E 9E 1E 11E 12E l. Autumn (OND); hpa Deep convection - - Inject CH 4 -rich air mass into the upper tropospheric region. Chandra et al., ACP (217) (in review)

15 XCH 4 (ppb) Contribution from different layers (%) X p CH 4 (ppb) Contributions of vertical layers in XCH 4 mixing ratios UA (σ p =.2 ) UT (σ p =.4.2) EIGP MT2 (σ p =.6.4) MT1 (σ p =.8.6) LT (σ p = 1..8) GOSAT ACTM_AGS ACTM_CTL South India GOSAT ACTM_AGS ACTM_CTL Arid India AI WIGP EIGP NEI EI CI WI SP SI AS BOB 6 4 EIGP South India Arid India 1 Region Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan UA UT MT2 MT1 LT gch 4 m -2 mon -1

16 Calculation of XCH 4 XCH 4 is calculated from the ACTM profile using the formula i CH 4 (i) p i, where i is the model level of thickness p i XCH 4 = Σ n=2 6 CH 4 (n) * [(σ p (n) + σ p (n-1))/2 - (σ p (n) + σ p (n+1))/2] For the first layer (n =1) XCH 4 = Σ n=1 CH 4 (n) * [1- (σ p (n) + σ p (n+1))/2] where n = number of vertical sigma pressure layer, σ p = sigma pressure level

17 GOSAT ACTM_AGS ACTM_CTL

18 GOSAT ACTM_AGS ACTM_CTL

19 Columnar loss rate of CH4 16 Arid India WIGP EIGP Northeast India Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Total columnar CH 4 loss rate (ppb/day) Western India Jan Apr Jul Oct Jan Arabian Sea Central India Jan Apr Jul Oct Jan Bay of Bengal East India Jan Apr Jul Oct Jan South India Jan Apr Jul Oct Jan 8 4 Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan

20 Temperature impact (1-3 K) Anthropogenic GHGs Why do we care about methane? Second most important drivers of climate change. Resulting atmospheric drivers CO 2 CH 4 Halocarbons N 2 O CO Radiative forcing relative to 175 (W m -2 ) Importance of methane as a short lived climate forcer (SLCF). Temperature response to 1-year pulse (8) of emissions Time Horizon (yr) Curbing CH 4 emissions will more helpful than CO 2 to fight against global warming at inter-decadal time scale. Source: IPCC, 213

21 CH 4 emission : complexities and uniqueness of ACTM inversion Source types Natural: VISIT: Wetl & Rice GISS: Termite GFED: Bio. Burn SRON: Ocean SRON: MudVolcano Anthropogenic: (EDGAR4.2) IPCC_1A (transport) IPCC_1B (Fugitive) IPCC_2 (Industry) IPCC_4A (Ent. Ferm.) Soil sink: VISIT Chemical loss: OH: Spivakovsky/scl Cl/O 1 D: ACTM CH 4 ags CH 4 e42 CH 4 fix CH 4 fug CH 4 ong CH 4 enf CH 4 ctl Trends : come from Anthropogenic emissions Variability : are mainly due to Natural emissions Inversion results are dependent on the choice of prior flux So inversions are run for 7 ensemble cases The outliers are decided by independent aircraft measurements

22 Challenges Individual sources of CH 4 remain highly uncertain. In-situ observations - Improve our understanding of various CH 4 sources, but the observation stations are sparsely distributed. Observations from space have transformed the condition from data-poor to data-rich over past 2 years.

23 Challenges Regional emissions of CH 4 remains highly uncertain particular over Asian region, which is one of the most significant areas of CH 4 emissions. Where is the source of CH 4 in Asia? Satellite observations from space have the potential to captured the spatial and temporal variability in CH4 for most part of glob landin

24 Model Descriptions Atmospheric general circulation model (AGCM)-based CTM (i.e., JAMSTEC s ACTM). Meteorological field Anthropogenic EDGAR42FT212 (213) Wetlands and rice paddies Biomass burning Resolutions Japan Meteorological Agency reanalysis fields (vr., JRA-55). VISIT terrestrial ecosystem model Goddard Institute for Space Studies (GISS) and Global and Global Fire Emission Database (GFED) version 3.2 ~ horizontal and 67 vertical sigma-pressure levels Atmospheric molar fractions of CH 4 have been simulated using an ensemble of 3 cases of a priori emission scenarios. AGS CTL All emission sectors in EDGAR42FT kept constant at the values for, except for the emissions from agricultural soils. EDGAR32/VISIT/GISS

25 Columnar Methane (XCH4) UA UT MT2 MT1 LT hpa hpa 4 hpa 6 hpa 8 hpa 1 hpa hpa

26 Methane should be part of climate policy but for reasons totally different than CO 2 It addresses climate change on time scales of decades which we care about Loss of Arctic sea ice, seal level rise, rain during ski season It has air quality co-benefits Methane is a major precursor of background tropospheric ozone It is an alternative to geoengineering by aerosol injections Both address near-term climate change which do you prefer? Reducing methane emissions can be easy to do and make money Fix leaks from oil/gas super-emitters, capture methane from landfills Climate policy should not use a single time horizon for metrics; reporting both 2-year and 1-year GWPs would be a simple solution

27 The GOSAT observational record orbit track 3 cross-track 1-km pixels 25 km apart CO 2 proxy retrieval [Parker et al, 211] Mean single-retrieval precision 13 ppb Use here data for 6/9-12/211

28 THE ART AND SCIENCE OF CLIMATE MODEL TUNING (this has relevance to solving OH issues in CTMs) Hourdin et al., BAMS, 217 We survey the rationale and diversity of approaches for tuning, a fundamental aspect of climate modeling, which should be more systematically documented and taken into account in multimodel analysis. Global absorbed shortwave radiation (ASR, full curve) and outgoing radiation (OLR, dashed) at top of atmosphere. The squares and diamonds correspond to default values retained after a tuning phase (for GFDL and IPSL-CM they correspond to the values retained for CMIP5, but because the experiments were redone with recent versions of the same models, the balance is not completely satisfied with the selected values). Example of tuning approach for the ECHAM model (after Mauritsen et al. 212). The figure illustrates the major uncertainty in climaterelated stratiform liquid and ice clouds and shallow and deep convective clouds. The gray curve to the left represents tropospheric temperatures, and the dashed line is the top of the boundary layer. Parameters are (a) convective cloud mass flux above the level of nonbuoyancy, (b) shallow convective cloud lateral entrainment rate, (c) deep convective cloud lateral entrainment rate, (d) convective cloud water conversion rate to rain, (e) liquid cloud homogeneity, (f) liquid cloud water conversion rate to rain, (g) ice cloud homogeneity, and (h) ice particle fall velocity. The colored curves correspond to three configurations of the GFDL CM3 model. CM3 denotes the CMIP5 model, while CM3c and CM3w denote alternate configurations with large and smaller, respectively, cooling from cloud aerosol interactions.

29 Climate and Clean Air Coalition/CCAC) Launch of the Climate and Clean Air Coalition to Reduce Short Lived Climate Pollutants, Feb 17, 212. Source: US Department of State SLCP: CH4 Tropospheric Ozone Black Carbon 29

30 What is GOSAT? GOSAT(Greenhouse gases Observing SATellite) GOSAT is the first satellite that dedicated to greenhouse-gasmonitoring. Targets of GOSAT are CO 2 and CH 4 Onboard sensor is named TANSO-FTS (Fourier Transform Spectrometer) GOSAT collected more than 6-years of record as of Milestone * * * * * Launch in operation in space Current ground-based observation network Advantage of GOSAT is its wide spatial coverage