U.S

Climate Process Modeling And Science Teams CPT * A program for improving climate model elements * previously known as CLIVAR process modeling team David Battisti Chris Bretherton David M. Legler U.S. CLIVAR Office www.usclivar.org legler@usclivar.org CCSM2 Annual Mean Sfc Stress CCSM2 Annual Mean Precip Model Obs Diff U.S. CLIVAR Climate Process Teams 2 Motivation • Improving (reducing uncertainties in) coupled climate models can be achieved (in part) by reducing uncertainties associated with processes • Model validation/intercomparison projects (e.g. CMIP) highlight model uncertainties, but are not sufficiently focused to indicate how processes contribute to these uncertainties • The large gap between process oriented research efforts and climate model development as well as the lack of resources available for diagnosing and testing physical parameterizations in the context of coupled model systems impedes improvement in coupled models U.S. CLIVAR Climate Process Teams 3 Process experiments Data, knowledge Climate Process Teams Cooperative Development Process model development Climate model development & improvement Deliverables Documented observations Improved parameterizations Impact evaluation Plans for additional process studies Observing requirements Lack of focus, little interaction U.S. CLIVAR Climate Process Teams 4 CPTs Briefly • Teams of observational scientists, diagnostic scientists, process modelers, coupled modelers, data assimilation systems developers, organized around processes, would focus on quantifying and reducing uncertainties associated with these processes U.S. CLIVAR Climate Process Teams 5 CPT Objectives • Speed the improvement of coupled models, data assimilation systems, and model components by – Parameterizing the important processes not included explicitly in – Transferring theoretical and process-model understanding into – Sharpening our understanding of how particular physical processes – Identifying sustained observational requirements required by impact the climate system; climate models for these parameterizations; and improved treatment of processes in climate models; climate models; – Identifying additional process studies necessary to reduce uncertainties associated with important climate model processes/parameterizations. U.S. CLIVAR Climate Process Teams 6 Approach • Provide resources to forge teams of observational scientists, diagnostic scientists, process modelers, and (one or more) coupled model developers and data assimilation system developers to: – Establish open pathways of communications – Encourage active mechanisms for exchange of information (e.g. – Focus on long-term interaction & deliverables (not necessarily manuscripts) that lead to demonstrated advances in climate modeling CPTs are NOT: visiting programs) •Business as usual... •Just travel money for an annual meeting... U.S. CLIVAR Climate Process Teams 7 Possible Activities for CPTs • Assessing modeling capabilities – Develop process diagnostics – Assess consistency of popular parameterizations with • Existing observations • Detailed process models • Other implementations • Model sensitivity of climate & variability to process uncertainties – Single-model sensitivities – Model intercomparisons (CMIP subprojects) – Diagnosis of model errors attributable to errors in process parameterizations – Determine accuracy requirements needed to capture main climate feedbacks – Produce observational suites useful for diagnostic studies • Best use of observations – Upscaling of local process measurements to needed space/time scales – Quantify how errors associated with process uncertainties affect critical products – Establish observing requirements for parameterizations U.S. CLIVAR • Parameterization improvement – Engage team members in development of improved parameterizations – Evaluate new parameterizations – Plan future process studies to address unresolved uncertainties 8 Climate Process Teams Example processes/topics • Ocean processes (e.g. diapycnal mixing) • Atmosphere processes (e.g. deep convection) • Surface fluxes (e.g. best use of the observational datasets) U.S. CLIVAR Climate Process Teams 9 CPT for Diapycnal Ocean Mixing • Diapycnal mixing is main physics of ocean models – – – – – – – Internal wave mixing Diffusivity in the EUC Boundary mixing Abyssal mixing (tides and topography) Gravity wave drag in the ACC Enhanced mixed layer turbulence by gravity waves Entrainment into gravity currents • Current parameterizations inadequate • Significant impact on simulated circulations or their stability or sensitivity U.S. CLIVAR Climate Process Teams 10 Emerging component - DOME (Dynamics of Overflow Mixing and Entrainment) • Model intercomparisons – High-res OGCM’s – Non-hydrostatic models – Z-coord, sigma-coord, isopycnal • • • 3-phase intercomparison – Idealized – Gibraltar and Denmark Straits – Atlantic circulation • • • • • • Close working arrangement between OGCMs and process modelers Active theory community Data from Med Outflow very valuable (Barringer and Price) Strong influence on stability of thermohaline circulation (Price and Yang) Significant impact on mean circulation (Chassignet) Faroe Bank Channel observations begun Need to invigorate links to observational community Underway - develop science plan U.S. CLIVAR Climate Process Teams 11 CPT for Atmospheric Deep Convection Current efforts include: CCSM/Atm Modeling Working Group • • • • Good discussion forum for bringing global modelers together Limited participation from process scientists Limited time for interaction/democratic Few dedicated resources for interaction GEWEX Cloud System Study WG2/ARM • • • Emphasizes intercomparison rather than detailed diagnostics Limited participation from major modeling groups ARM support requires focus on ARM observations - may or may not address most pressing problems Large, cumbersome groups make for slow progress • U.S. CLIVAR Climate Process Teams 12 CPT for Atmospheric Deep Convection (2) • Needs – Dedicated cross-cutting group (3-5 yrs) – Explicit funding to support novel diagnostics, coding/testing of improved parameterizations – Global modelers identify critical problems related to convection (e.g. split ITCZ, cloud-radiative feedback, diurnal cycle) and set 3– yr goals • Funding – Support scientist at each modeling center doing diagnostics and testing code improvements - responds to entire group – Support for process scientists to pursue activities relevant to CPT – Travel funding for an annual meeting U.S. CLIVAR Climate Process Teams 13 CPT for Atmospheric Deep Convection (3) • Team Composition – Parameterization developers from NCAR, GFDL, NCEP (Hack, Donner, Pan, + 1-2 other groups) – Process Scientists with expertise in • • • • • In-situ obs (Raymond, Mapes, Johnson) Satellite obs (Wielicki) Cloud-resolving models (Tao) Diagnostics and data assim (DeMott, Hou) SCM and novel parameterization approaches (Randall) U.S. CLIVAR Climate Process Teams 14 CPT Formulation • What characteristics define a CPT – Team Topic/Process – Team Composition – Team Plans and Deliverables U.S. CLIVAR Climate Process Teams 15 Implementation • Significant investment - $1M (USD) per team per year • Pilot-phase – – – – High-priority processes Observations already/nearly in hand Relatively short lifetime Reduced number of deliverables • Additional and more robust CPTs formed in the future U.S. CLIVAR Climate Process Teams 16 Issues • Team scoping – To be effective, must target modeling systems. Thus how do we scope single process, suite of processes, or the model system...optimal team focus? • Team accountability/metrics – How will CPTs be held accountable for stated objectives? U.S. CLIVAR Climate Process Teams 17 SSG Consideration • Climate Process Teams as a new (better) approach to link process-oriented research and modeling? U.S. CLIVAR Climate Process Teams 18 equired from: » attorney general » deputy attorney general » assistant attorney general for the criminal division 30 Types of Enforcement Legislative history, Senator Kohl: – "... we have carefully drafted these measures so that they can only be used in flagrant and egregious cases of information theft.“ Take it very, very seriously – Rule of Thumb: » Turn in anyone who approaches you with information 31 Definition of Trade Secrets 18 U.S.C. Sec. 1839(3) Basically the same as – Uniform Trade Secrets Act – Common Law 32 Types of Misappropriation Offenses Defined Economic Espionage: 18 U.S.C. 1831 – Knowing or intentional, – Theft of a trade secret, to – Benefit a » Foreign government » Instrumentality » Agent 33 Types of Misappropriation Offenses Defined Theft of Trade Secrets: 18 U.S.C. 1832 – – – – Knowing or intentional, Conversion of a trade secret, Related to a product in commerce, to Injure the owner of the trade secret 34 Illegal Acts Violation to knowingly: – (1) Steal or obtain without authorization or by deception – (2) Copy or convey without authorization – (3) Receive, buy, or possess knowing the same to have been improperly obtained – (4) Attempt 1-3 – (5) Conspire to commit 1-3 with an active participant 35 Penalty for Violation Fines and imprisonment  Benefit of a foreign government (Espionage) – Individual: fine up to $500,000 and/or up to 15 years imprisonment – Organization: fine up to $10,000,000  Related to a product in commerce (Theft) – Individual: fine and/or up to 10 years imprisonment – Organization: fine up to $5,000,000 36 Penalty for Violation Criminal Forfeiture Violators forfeit to the United States: – Any proceeds or property derived as a result of the violation – Any property used in commission of the violation 37 Construction with Other Laws Section 1838 Federal Criminal law does not affect or preempt – Other Federal law – State trade secret law – State contract law Possible to violate both a state trade secret law and the Federal criminal law 38 Potential Defenses Same as in Civil Cases – Reverse Engineering » "If someone has lawfully gained access to a trade secret and can replicate it without violating copyright, patent or this law, then that form of 'reverse engineering' should be fine" » General knowledge and experience » "... trade secrets are carefully defined so that the general knowledge and experience that a person gains from working at a job is not covered." » No knowledge - no intent 39 THANK YOU FOR YOUR TIME Questions?