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ECLIPSE. E valuating the CL imate and Air Quality I m P acts of S hort-liv E d Pollutants. NILU,CICERO, METNO, IIASA, Met Office, U. Reading, UPMC, FORTH, U. Leipzig, U. Peking, U. Tsinghua. Overview. Motivation for studying short-lived pollutants
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ECLIPSE Evaluating the CLimate and Air Quality ImPacts of Short-livEd Pollutants NILU,CICERO, METNO, IIASA, Met Office, U. Reading, UPMC, FORTH, U. Leipzig, U. Peking, U. Tsinghua
Overview • Motivation for studying short-lived pollutants • Applications of climate metrics for SLCPs • ECLIPSE structure • Conclusions
Importance of short-lived pollutants Control of SLCPs more important in short-term <30-40 years Control of CO2 more important in long-term
Climate metrics • Often used to compare and trade climate agents • CO2 as reference gas • GWP100=AGWP100/AGWPCO2100 • The absolute metrics parameterise climate impact • AGWP(t): total energy input from an emission (J kg-1) up to time t • AGTP(t): surface temperature change (K kg-1) at time t • Can calculate temperature impacts of any policy knowing ΔE(t) and AGTP(t) • SLCPs: heterogeneous impacts, vary with latitude band l. • ARTPl(t) (Regional rather than Global)
Applications • Can use regional climate metrics (ARTPs) to identify regional climate impacts of any emission profile • Used HTAP results of regional forcing(Collins et al. 2013) • BC, OC, SO2, NOx, VOC, CO, CH4, (no NH3 in HTAP) • scaled by Bond et al. efficiencies for BC, OC (incl. indirect effect)
Calculating metrics Combination of chemistry and climate models GCM (climate) ∆T(t) ∆F AGTP(t) × ∆F(t) ∆B(t) RTM (radiation) CTM (chemistry) ∆E CCM (chemistry-radiation) All together in one Earth System model ESM (chemistry-radiation-climate) ∆T(t), ∆P(t) AGTP(t) ∆E
Emissions • Generated by IIASA • More realistic than CMIP5 RCPs • Available for use by other projects • PEGASOS, ECLAIRE • Will generate “climate optimum” pollution controls
ECLIPSE model output processing Overall Model Performance Visualization Via AeroCom web interface Where compared ? Correlated ? Regional Normalized Bias ? Local month to month variation? Histogram? Zonal Absolute Bias ?
Hippo1: Oslo CTM2 and ECLIPSE emissions Oslo CTM2 and RCP emissions • Vertical profile of BC at high latitudes: Hippo1 Jan 2009 Schwarz et al. (GRL 2009) DJF mean concentration north of 65 degrees: ← ECLIPSE 2009 simulations Sensitivity tests CTM2/3→ Totally dominated by FFBF BC!
WP4 – Climate forcing The overall goal of this work package is to determine climate forcing as a function of emission sector and emission region for a comprehensive set of region/sector combinations.
Radiative forcings • In ECLIPSE we use effective RF (ERF) as well as RF. • ERF includes fast feedbacks • Fixed SST, but allows temperature profile and clouds to respond • Better estimate of temperature response than RF
RF East Asia Europe Shipping Radiative Forcing per kg emitted RFs give clear signal (fixed meteorology) Can detect differences between regions and season
ERFs • ERFs give noisy signal (varying meteorology) • Can’t so easily detect differences between regions and season
Coupled chemistry-climate simulations temperature Global reduction of seven SLCFs Control simulation: WMGHG concentrations and SLCF emissions fixed simulation time 50 yrs Analyse climate responses: - temperature, precipitation, run off, cloudiness, ecosystem productivity • Will be even more noisy than ERFs • Need to run for very long time 50-100 years
Conclusions • Improve understanding of key processes (esp. in Artic) • Produce Air Quality and climate metrics relating emission changes to regional impacts (esp. in Arctic) • Compare separate steps with full Earth System Model • These metrics can in principle be used to assess impacts of any scenario • They will be used calculate an “optimum” set of emission control measures balancing air quality and climate • The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 282688 - ECLIPSE