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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal style='background:white'><span style='color:black'><a href="https://statistics.yale.edu/" title="Department of Statistics and Data Science
"><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#286DC0;text-decoration:none'><img border=0 width=150 height=49 style='width:1.5625in;height:.5104in' id=logo src="cid:image001.jpg@01D81742.179EE7C0" alt="Department of Statistics and Data Science
"></span></a> <a href="https://statistics.yale.edu/" title=Home><b><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#286DC0'>Department of Statistics and Data Science Seminar </span></b></a></span><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#222222'><o:p></o:p></span></p><h1 style='mso-margin-top-alt:.1in;margin-right:0in;margin-bottom:0in;margin-left:0in;background:white'><span style='font-size:23.0pt;font-family:"Verdana",sans-serif;color:#003C76;text-transform:uppercase;font-weight:normal'>MEGAN LICKLEY</span><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'>, <span class=odd>MIT</span></span><span style='font-size:23.0pt;font-family:"Verdana",sans-serif;color:#003C76;text-transform:uppercase;font-weight:normal'><o:p></o:p></span></h1><p class=MsoNormal style='background:white'><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f">
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</v:shape><![endif]--><![if !vml]><img width=110 height=132 style='width:1.1458in;height:1.375in' src="cid:image004.jpg@01D81837.F574CC90" align=left hspace=12 v:shapes="Picture_x0020_2"><![endif]><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span class=date-display-single><span style='font-size:13.5pt;font-family:"Verdana",sans-serif;color:#003C76'>Wednesday, February 02, 2022</span></span><span class=date-display-single><span style='font-size:13.5pt;color:#003C76'><o:p></o:p></span></span></p><p class=MsoNormal style='background:white'><span class=date-display-start><span style='font-size:13.5pt;font-family:"Verdana",sans-serif;color:#003C76'>4:00PM</span></span><span class=date-display-range><span style='font-size:13.5pt;font-family:"Verdana",sans-serif;color:#003C76'> to </span></span><span class=date-display-end><span style='font-size:13.5pt;font-family:"Verdana",sans-serif;color:#003C76'>5:00PM</span></span><o:p></o:p></p><p class=MsoNormal style='background:white'><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'>Zoom: <a href="https://yale.zoom.us/j/97240723016">https://yale.zoom.us/j/97240723016</a><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'><a href="https://eapsweb.mit.edu/people/mlickley"><span style='font-size:12.0pt;color:#003C76'>Website</span></a><o:p></o:p></span></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:15.0pt;font-family:"Verdana",sans-serif;color:#222222'>Title: Targeting climate science and data science at policies<o:p></o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'>Information and Abstract: <o:p></o:p></span></b></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;background:white;box-sizing: inherit'><span style='font-size:13.0pt;font-family:"Verdana",sans-serif;color:#222222'>Uncertainty in climate model simulations complicates decision-making and undermines optimal policy choices. This talk will discuss how data science methods can reduce uncertainty in climate models and better inform key policy decisions. I will focus on methods addressing the evaluation of global compliance with the Montreal Protocol, the only binding UN treaty ratified by all member states to regulate greenhouse gas production. Large uncertainties in atmospheric lifetimes of gases regulated by the Protocol are large enough to obscure evaluation of global emissions. This talk will show how jointly considering production and emissions of multiple gases can reduce uncertainty in each and ultimately better constrain ongoing production and emissions estimates. <o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>