Seamless Weather and Climate Prediction Jagadish Shukla George Mason University (GMU), USA Institute of Global Environment and Society (IGES) “Revolution in Climate Prediction is Both Necessary and Possible” Shukla, Hagedorn, Hoskins, Kinter, Marotzke, Miller, Palmer, and Slingo, BAMS 2009 Center of Ocean-Land- Atmosphere studies Climate Test Bed Seminar Series 10 February

Outline 1.Introduction: “Seamless” (WCRP) 2.Generalized Seamless Prediction Concept 3.Model Limitations and Successes 4.Role of Tropical Convection/Heating 5.Model Fidelity and Predictability 6.World Modeling Summit for Climate Prediction 7.Suggestions to Revolutionized Climate Prediction Center of Ocean-Land- Atmosphere studies 2

Evolution of the Concept of Seamless Prediction in WCRP 2002: In response to proposals by J. Shukla to launch the World Climate Experiment, and assess predictability of the climate system, the Joint Scientific Committee (JSC) of WCRP (Hobart, Tasmania) established a Task Force on Predictability Assessment of the Climate System. Members: B. Hoskins, J. Church, J. Shukla (Seamless Prediction concept introduced) 2004: JSC established a Talk Force on COPES Members: R. Barry (CLiC), D. Carson (WCRP), B. Kirtman (TFSP), J. Matsumoto (CEOP), J. Mitchell (WGCM), K. Puri (WGNE), A. O’Neill (SPARC), J. Shukla (JSC, WMP), P.K. Taylor, K. Trenberth (JSC, WOAP), M. Visbeck (CLIVAR), E. Wood (GEWEX) 2005: WCRP/COPES strategic framework and WCRP Modeling Panel adopted the concept of seamless prediction as the organizing principle for WCRP modeling. COPES: Coordinated Observation and Prediction of the Earth System. (WCRP-123, WMO/TD-No. 1291, 2005, pp 1-59) Center of Ocean-Land- Atmosphere studies 3

GEWEX 1988  SPARC 1992  WGNE WGCM WGSF SOLAS > CLIVAR 1995  CliC 2000  WCRP Observation & Assmilation Panel WCRP Modelling Panel Coordinated Observation and Prediction of the Earth System 4

The WCRP Strategic Framework AIM To facilitate analysis & prediction of Earth system variability & change for use in an increasing range of practical applications of direct relevance, benefit & value to society Coordinated Observation and Prediction of the Earth System (WCRP-COPES) Center of Ocean-Land- Atmosphere studies 5

Coordination of WCRP Modeling Activities WGNE, TFSP, GMPP Intra-Seasonal Prediction (1-30 days) WGSIP, TFSP, GMPP, CliC, SPARC, WGOMD Seasonal Prediction (1-100 days) WGNE Weather Prediction (1-10 days) WGCM Climate Change Prediction (1-100,000 days) WGCM, WGOMD Decadal Prediction (1-10,000 days) WGSIP, TFSP, WGCM, WGOMD Interannual Prediction (1-1,000 days) Center of Ocean-Land- Atmosphere studies 6

Seamless Prediction Problem 1.There is now a new perspective of a continuum of prediction problems, with a blurring of the distinction between shorter-term predictions and longer-term climate projections. Increasingly, decadal and century-long climate projection will become an initial-value problem requiring knowledge of the current observed state of the atmosphere, the oceans, cryosphere, and land surface (including soil moisture, vegetation, etc.) in order to produce the best climate projections as well as state-of-the-art decadal and interannual predictions. Center of Ocean-Land- Atmosphere studies WCRP Strategic Framework (COPES) 7

Seamless Prediction Problem 2. The shorter time-scales and weather are known to be important in influencing the longer-time-scale behaviour. In addition, the regional impacts of longer-time-scale changes will be felt by society mainly through the resulting changes in the character of the shorter time-scales, including extreme events. In recognition of this, climate models are being run with the highest possible resolutions, resolutions that were employed in the best weather forecast models only a few years ago. 3. Even though the prediction problem itself is seamless, the best practical approach to it may be described as unified: models aimed at different time-scales and phenomena may have large commonality but place emphasis on different aspects of the system. Center of Ocean-Land- Atmosphere studies WCRP Strategic Framework (COPES) 8

Seamless Prediction Since climate in a region is an ensemble of weather events, understanding and prediction of regional climate variability and climate change, including changes in extreme events, will require a unified initial value approach that encompasses weather, blocking, intraseasonal oscillations, MJO, PNA, NAO, ENSO, PDO, THC, etc. and climate change, in a seamless framework. Center of Ocean-Land- Atmosphere studies 9

A Generalized Seamless Prediction Concept Seamless across: Space scales (clouds to global climate system) Time scales (minutes to centuries; multi-scale interactions) Phenomena (Convection-MJO-ENSO-PDO-AMO-Climate Change) Scientific disciplines (weather, climate, Earth system, biodiversity, socio-economics) Institutions (academic, government, corporations, intra- institutional labs/divisions) Political boundaries (local, state, national and international governments) Center of Ocean-Land- Atmosphere studies 10

Global change Global change Some Examples of Seamless Processes Tropical Convection Tropical Convection(SST) (SST) Rossby Waves Rossby Waves(Atmosphere) (Atmosphere) North America Forest Fires North America Forest Fires(Land) (Land) Surface Wind Surface Wind(Atmosphere) (Atmosphere) Eurasian Snow Eurasian Snow(Cryosphere) (Cryosphere) Pacific/IO SST Pacific/IO SST(Ocean) (Ocean) Walker cell Walker cell(Atmosphere) (Atmosphere) Asian Monsoon Asian Monsoon(Land) (Land) Propagation down Propagation down Extra Trop. Surface Winds Extra Trop. Surface Winds Upp. Stratosphere Circ. Upp. Stratosphere Circ. ENSO ENSO ISO/MJO ISO/MJO Global Warming Global Warming Regional SSTA Regional SSTA Hurricanes Hurricanes Persistent Drought Persistent Drought(Land) (Land) Influence ENSO Influence ENSO(Ocean) (Ocean) Monson Droughts Monson Droughts(Atmosphere) (Atmosphere) Wet/Dry soil, Ts Wet/Dry soil, Ts(Land) (Land) Center of Ocean-Land- Atmosphere studies 11

From Cyclone Resolving Global Models to Cloud System Resolving Global Models 1.Planetary Scale Resolving Models (1970~): Δx~500Km 2.Cyclone Resolving Models (1980~): Δx~ Km 3.Mesoscale Resolving Models (1990~): Δx~10-30Km 4.Cloud System Resolving Models (2000 ~): Δx~3-5Km Organized Convection Cloud System Mesoscale System Synoptic Scale Planetary Scale Convective Heating Convective Heating MJO ENSO Climate Change Climate Change Seamless Prediction of Weather and Climate Center of Ocean-Land- Atmosphere studies 12

Important Issues and Discussions 1. Lack of comprehensive model development efforts globally 2. Low resolution IPCC models can not simulate blocking 3. Regional downscaling of climate change: questionable 4. Seamless prediction: IPCC projections as “Initial Value Problem” 5. Insufficient computing for next generation models 6. Realism versus complexity: chemistry, biology; physical climate 7. Data assimilation for next generation models 8. Lack of progress in ENSO prediction (model error, IC) 9. Common data management strategy for all WCRP activities 10. Joint WCRP-IGBP-THORPEX effort for models and data assimilation WMP reports to JSC (Zanzibar, 2007) that climate models have serious problems Center of Ocean-Land- Atmosphere studies 13

Systematic Error: MSLP (NDJ) 14

Infamous Double ITCZ Problem 15

Annual Cycle of SST Climatology 4-6 month forecast, APCC/CliPAS & DEMETER CGCMs Calendar Month Center of Ocean-Land- Atmosphere studies Jin et al Climate Dynamics 16

NINO 3.4 Index (Observed and CFS) HadSSTv1.1 CFS long run Calendar year Jin and Kinter 2009 Climate Dynamics Center of Ocean-Land- Atmosphere studies 17

15-member CFS reforecasts Skill in SST Anomaly Prediction for Nino3.4 DJF 1981/82 to AMJ 2004 Center of Ocean-Land- Atmosphere studies

NINO3: Warm minus Cold composite SST anomalies Influence of Systematic Error on CFS Forecast Skill  Warm composite (82/83, 86/87, 91/92, 97/98) - Cold composite (84/85, 88/89, 98/99, 99/00)  Dashed lines denote composite for Hindcasts at different lead times Observation CFS long run (Hindcast composite) Forecast lead month Correlation CORR. with respect to lead month based on 1 st SEOF mode of SST Correlation between 1 st PCs based on observation and hindcasts at different lead times Correlation between 1 st PCs based on long run and hindcasts at different lead times  Model Flaw: Slow coupled dynamics of CGCM 19 Jin and Kinter 2009, Climate Dynamics

Fundamental barriers to advancing weather and climate diagnosis and prediction on timescales from days to years are (partly) (almost entirely?) attributable to gaps in knowledge and the limited capability of contemporary operational and research numerical prediction systems to represent precipitating convection and its multi-scale organization, particularly in the tropics. (Moncrieff, Shapiro, Slingo, Molteni, 2007) Center of Ocean-Land- Atmosphere studies 20

Shukla and Kinter 2006 Center of Ocean-Land- Atmosphere studies 21 Effect of SST Anomaly

Center of Ocean-Land- Atmosphere studies Rainfall Zonal Wind The atmosphere is so strongly forced by the underlying ocean that integrations with fairly large differences in the atmospheric initial conditions converge, when forced by the same SST (Shukla, 1982). 22

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Center of Ocean-Land- Atmosphere studies Evolution of Climate Models Model-simulated and observed, 1983 minus 1989 Rainfall (mm day-1)500 hPa GPH anomaly (m) KUO R15 RAS R40 Observed 24

Center of Ocean-Land- Atmosphere studies Percent Variance of PNA region explained by Tropical SST Probability Distribution Boreal Winter (DJF) Rainfall Variance in AGCMs 25

Evolution of Climate Models Model-simulated and observed rainfall anomaly (mm day -1 ) 1983 minus 1989 Center of Ocean-Land- Atmosphere studies 26

Evolution of Climate Models Model-simulated and observed 500 hPa height anomaly (m) 1983 minus

MRF8: high, middle, low clouds allowed to exist MRF9: Only high cloud allowed to exist over regions of tropical deep convection MRF8MRF9 Center of Ocean-Land- Atmosphere studies 28 Thanks to Arun Kumar (CPC/NCEP)

Center of Ocean-Land- Atmosphere studies 29 Kumar et al Journal of Climate MRF8: high, middle, low clouds allowed to exist MRF9: Only high cloud allowed to exist over regions of tropical deep convection

Vintage 1980 GFDL AGCM (Lau, 1997, BAMS) Note: amplitude of model response quite weak; structure is PNA rather than ENSO forced Center of Ocean-Land- Atmosphere studies Note: estimate of predictability depends on model fidelity 30

Vintage 2000 AGCM 31

Bjorn Stevens, UCLA World Modelling Summit, ECMWF, May Center of Ocean-Land- Atmosphere studies Annually & Zonally Averaged Reflected SW Radiation

33 Center of Ocean-Land- Atmosphere studies Bjorn Stevens, UCLA World Modelling Summit, ECMWF, May 2008 Annually & Zonally Averaged SW Radiation (AR4)

34 Center of Ocean-Land- Atmosphere studies Clouds as Ultimate, rather than Proximate, Sources of Bias Bjorn Stevens, UCLA World Modelling Summit, ECMWF, May 2008

1. Predictions of climate change depends on the climate model’s fidelity in simulating the current climate. 2. Models with low fidelity in simulating climate statistics (mean and variability) have low skill in predicting seasonal climate anomalies. Conjectures Center of Ocean-Land- Atmosphere studies 35

Model sensitivity versus model relative entropy for 13 IPCC AR4 models. Sensitivity is defined as the surface air temperature change over land at the time of doubling of CO 2. Relative entropy is proportional to the model error in simulating current climate. Estimates of the uncertainty in the sensitivity (based on the average standard deviation among ensemble members for those models for which multiple realizations are available) are shown as vertical error bars. The line is a least-squares fit to the values. J. Shukla, T. DelSole, M. Fennessy, J. Kinter and D. Paolino Geophys. Research Letters, 33, doi /2005GL025579, 2006 Climate Model Fidelity and Projections of Climate Change 36

Climate Model Fidelity and Projections of Climate Change Relative Entropy: The relative entropy between two distributions, p 1 (x) and p 2 (x), is defined as (1) where the integral is a multiple integral over the range of the M- dimensional vector x. (2) where  j k is the mean of p j (x) in the kth season, representing the annual cycle,  j is the covariance matrix of p j (x), assumed independent of season and based on seasonal anomalies. The distribution of observed temperature is appropriately identified with p 1, and the distribution of model simulated temperature with p 2. Center of Ocean-Land- Atmosphere studies 37

Climate Model Fidelity and Projections of Climate Change Model vs. Model Relative Entropy with respect to MIROC high-resolution Center of Ocean-Land- Atmosphere studies 38

Climate Model Fidelity and Climate Prediction Interim Conclusions: If we conjecture that models that better simulate the present climate should be considered more credible in projecting the future climate change, then this relationship suggests that the actual changes in global warming will be closer to the highest projected estimates among the current generation of models used in IPCC AR4. Lack of understanding of causes of model differences – is source of uncertainty in predicting climate change. Question: Will AR5 be any different? Center of Ocean-Land- Atmosphere studies 39

1. Predictions of climate change depends on the climate model’s fidelity in simulating the current climate. 2. Models with low fidelity in simulating climate statistics (mean and variability) have low skill in predicting seasonal climate anomalies. Conjectures Center of Ocean-Land- Atmosphere studies 40

DelSole (research in progress) Center of Ocean-Land- Atmosphere studies 41

Examples of Success Understanding of Dynamics and Physics of A, O, L Climate System Numerical Weather Prediction (NWP) –Steady improvement in skill Dynamical Seasonal Prediction (DSP) –Prediction of large Amp. ENSO Climate Change Prediction (“IPCC”) –Human activities are changing climate Center of Ocean-Land- Atmosphere studies 42

43 Center of Ocean-Land- Atmosphere studies

Time series VW 850 tropics 44

Center of Ocean-Land- Atmosphere studies ERA Forecast Verification Anomaly Correlation of 500 hPa GPH, 20-90N 45

ERA Forecast Verification Anomaly Correlation of 500 hPa GPH, 20-90N Center of Ocean-Land- Atmosphere studies 46

Number of Northern Hemisphere Cyclones T255 ERA T159 T95 Jung

Nastrom & Gage,

dx= dx= 40km 25km 10km Spectra of Total KE log 10 k k -3 k -5/3 49

Masaki Satoh, Hirofumi Tomita, Hiroaki Miura, Shinichi Iga and Tomoe Nasuno, 2005: J. Earth Simulator, 3, 1-9. Closest attempt to global cloud resolving model so far … 54 layers, top at 40 km 15-second time step ~ 1 TF-day per simulated day Ocean-covered Earth Geodesic grid 3.5 km cell size, ~10 7 columns Running on Earth Simulator Center of Ocean-Land- Atmosphere studies 50

Obs. (Takayabu et al. 1999) NICAM (7-km) Matsuno (AMS, 2007) Center of Ocean-Land- Atmosphere studies 51

52 Masaki Satoh, JAMSTEC World Modelling Summit, ECMWF, May 2008

Center of Ocean-Land- Atmosphere studies MJO in High Resolution Model A Madden-Julian Oscillation Event Realistically Simulated by a Global Cloud-Resolving Model. H. Miura, M. Satoh, T. Nasuno, A. T. Noda, and K. Oouchi Science, 1763 (2007); 318, DOI: /science (A) Infrared image from the Multi Functional Transport Satellite (MTSAT-1R) at 00:30 UaTC on 31 Dec (B) outgoing longwave radiation from the 3.5km-run averaged from 00:00 UTC to 01:30 UTC on 31 Dec

Towards a Hypothetical “Perfect” Model Replicate the statistical properties of the past observed climate –Means, variances, covariances, and patterns of covariability Utilize this model to estimate the limits of predicting the sequential evolution of climate variability Better model  Better prediction (??) Societal Needs Regional climate prediction from days to decades –Global cloud system resolving models are required Science based adaptation and mitigation strategies –Billion to trillion dollar decisions to be made by policymakers Optimum utilization of space and in-situ observation Center of Ocean-Land- Atmosphere studies 54

Revolution in Climate Prediction is Possible and Necessary Coupled Ocean-Land-Atmosphere Model ~2015 ~1 km x ~1 km (cloud-resolving) 100 levels (Unstructured, adaptive grids) ~100 m 10 levels Landscape-resolving ~10 km x ~10 km (eddy-resolving) 100 levels (Unstructured, adaptive grids) Assumption: Computing power enhancement by a factor of 10 6 Improved understanding of the coupled O-A-B-C-S interactions Improved understanding of the coupled O-A-B-C-S interactions Data assimilation & initialization of coupled O-A-B-C-S system Data assimilation & initialization of coupled O-A-B-C-S system Center of Ocean-Land- Atmosphere studies 55

Interim Conclusion The largest obstacles in realizing the potential predictability of weather and climate are inaccurate models and insufficient observations, rather than an intrinsic limit of predictability. –In the last 30 years, most improvements in weather forecast skill have arisen due to improvements in models and assimilation techniques The next big challenge is to build a hypothetical “perfect” model which can replicate the statistical properties of past observed climate (means, variances, covariances and patterns of covariability), and use this model to estimate the limits of weather and climate predictability –The model must represent ALL relevant phenomena, including ocean, atmosphere, and land surface processes and the interactions among them Lecture in the Lorenz Symposium, AMS, 2005 Center of Ocean-Land- Atmosphere studies 56

Events Leading to the Modelling Summit 1.WCRP established new strategic framework in 2004 (COPES). 2.COPES established WCRP Modelling Panel (WMP). 3.WMP reports to JSC (Zanzibar, 2007) that climate models have serious problems. 4.JSC asks WMP (Chair: J. Shukla) to organize World Modelling Summit (WMS). 5.WCRP forms WMS organizing committee. 6.WMS takes place at ECMWF (6-9 May 2008). Nearly 150 participants from all modelling centers of the world. Center of Ocean-Land- Atmosphere studies 57

Complexity Duration and/or Ensemble size Resolution Computing Resources 58

Weather and Climate Model Evolution  Computer power has increased 10 6 X since 1970s, but numerical models used for NWP and climate simulation have remained roughly the same  Same equations (non-hydrostatic)  Spectral or finite-diff. methods (fv cores; geodesic grids)  Simple parameterizations (some improvement)  Resolution: 4X in horizontal, 3X in vertical Accounts for ~ 10 3 X  Remaining 10 3 X:  Longer runs  Ensembles of model integrations  Computer power has increased 10 6 X since 1970s, but numerical models used for NWP and climate simulation have remained roughly the same  Same equations (non-hydrostatic)  Spectral or finite-diff. methods (fv cores; geodesic grids)  Simple parameterizations (some improvement)  Resolution: 4X in horizontal, 3X in vertical Accounts for ~ 10 3 X  Remaining 10 3 X:  Longer runs  Ensembles of model integrations (courtesy of David Randall) 59

Yelick, U.C. Berkeley World Modelling Summit, ECMWF, May Petaflop with ~1M Cores by 2008

Computing Capability & Model Grid Size (~km) Peak Rate:10 TFLOPS100 TFLOPS1 PFLOPS10 PFLOPS100 PFLOPS Cores 1,400 (2006) 12,000 (2008) ,000 (2009) ,000 (2011) 6,000,000? (20xx?) Global NWP 0 : 5-10 days/hr Seasonal 1 : days/day Decadal 1 : 5-10 yrs/day Climate Change 2 : yrs/day Range: Assumed efficiency of 10-40% 0 - Atmospheric General Circulation Model (AGCM; 100 levels) 1 - Coupled Ocean-Atmosphere-Land Model (CGCM; ~ 2X AGCM computation with 100-level OGCM) 2 - Earth System Model (with biogeochemical cycles) (ESM; ~ 2X CGCM computation) * Core counts above O(10 4 ) are unprecedented for weather or climate codes, so the last 3 columns require getting 3 orders of magnitude in scalable parallelization (scalar processors assumed; vector processors would have lower processor counts) Thanks to Jim Abeles (IBM) 61

Important Issues and Discussions 1. Lack of comprehensive model development efforts globally 2. Low resolution IPCC models can not simulate blocking 3. Regional downscaling of climate change: questionable 4. Seamless prediction: IPCC projections as “Initial Value Problem” 5. Insufficient computing for next generation models 6. Realism versus complexity: chemistry, biology; physical climate 7. Data assimilation for next generation models 8. Lack of progress in ENSO prediction (model error, IC) 9. Common data management strategy for all WCRP activities 10. Joint WCRP-IGBP-THORPEX effort for models and data assimilation WMP reports to JSC (Zanzibar, 2007) that climate models have serious problems Center of Ocean-Land- Atmosphere studies 62

Dr. Michel Beland Dr. Cecilia Bitz Dr. Gilbert Brunet Dr. Veronika Eyring Dr. Renate Hagedorn Dr. Brian Hoskins Dr. Christian Jakob Dr. Jim Kinter Dr. Herve LeTreut Dr. Jochem Marotzke Dr. Taroh Matsuno Dr. Gerald Meehl Dr. Martin Miller Dr. John Mitchell Dr. Antonio Navarra World Modelling Summit for Climate Prediction (International Organizing Committee) Dr. Carlos Nobre Dr. Tim Palmer Dr. Venkatchalam Ramaswamy Dr. David Randall Dr. Jagadish Shukla (Chair) Dr. Julia Slingo Dr. Kevin Trenberth WMO / WCRP Dr. Len Barrie Dr. John Church Dr. Ghassem Asrar JSC asks WMP (Chair: J. Shukla) to organize WMS Center of Ocean-Land- Atmosphere studies 63

1. Overview: societal drivers; current status of weather and climate modeling; strategies for seamless prediction; crucial hypotheses (Hoskins) 2. Strategies for next-generation modelling systems: balance between resolution and complexity; balance between multi-model and unified modeling framework; issues of parameterizing unresolved scales and regional models (Miller) 3. Prospects for current high-end computer systems and implications for model code design (Kinter) 4. Strategies for model evaluation, modelling experiments, and initialization for prediction of the coupled ocean-land-atmosphere climate system (Marotzke) 5. Strategies for revolutionizing climate prediction: enhancing human and computing resources; requirements and possible organizational frameworks (Slingo) World Modelling Summit for Climate Prediction (Themes and Theme leaders) Center of Ocean-Land- Atmosphere studies 64

WMS takes place at ECMWF (6-9 May 2008). Nearly 150 participants from all modelling centers of the world. Article in Nature, May 2008 Center of Ocean-Land- Atmosphere studies 65

Summary of WMS (summit declaration) Center of Ocean-Land- Atmosphere studies 66

Challenge The world recognizes that the consequences of global climate change constitute one of the most important threats facing humanity. The peoples, governments, and economies of the world must develop mitigation and adaptation strategies, which will require investments of trillions of dollars, to avoid the dire consequences of climate change. The development of reliable, science-based adaptation and mitigation strategies will only be possible through a revolution in regional climate predictions, supported by appropriate climate observations and assessment, and the delivery of this information to society. Center of Ocean-Land- Atmosphere studies 67

1.Most important requirement: Prediction of changes in the statistics of regional weather variations. 2. Models have serious problems and cannot provide information with accuracy required by society 3. “A revolution in climate prediction is necessary and possible.” (one of the most important declarations of the summit) 4. Proposal to establish a Climate Prediction Project 5. Enhance national centers 6. Establish a small number of climate research facilities for decadal prediction. Summary of Summit Declaration Center of Ocean-Land- Atmosphere studies 68

7. Dedicated high-end computing facilities are required (at least a thousand times more powerful than the currently available computers) 8. More computing power will help to enhance resolution and include complexity (e.g. biogeochemical cycles). 9. Global observations and assimilations are needed for prediction project. 10. Better estimates of uncertainties in climate prediction. 11. Collaboration between weather and climate prediction research communities (Seamless prediction). 12. Encourage the participation of young generation of climate modelers Summary of Summit Declaration Center of Ocean-Land- Atmosphere studies 69

There is a scientific basis for revolutionizing climate prediction The problem is beyond a person, a center, a nation … International collaboration is required International Research and Computational Facility to Revolutionize Climate Prediction Center of Ocean-Land- Atmosphere studies 70

International Research and Computational Facility to Revolutionize Climate Prediction 1. Computational Requirement: - Sustained Capability of 2 Petaflops by Sustained Capability of 10 Petaflops by 2015 Earth Simulator (sustained 7.5 Teraflops) takes 6 hours for 1 day forecast using 3.5 km global atmosphere model; ECMWF (sustained 2 Teraflops) takes 20 minutes for 10 day forecast using 24 km global model 2. Scientific Staff Requirement: - Team of 200 scientists to develop next generation climate model - Distributed team of 500 scientists (diagnostics, experiments) A computing capability of sustained 2 Petaflops will enable 100 years of integration of coupled ocean-atmosphere model of 5 km resolution in 1 month of real time Center of Ocean-Land- Atmosphere studies 71

International Research and Computational Facility to Revolutionize Climate Prediction Examples of International Collaboration CERN: European Organization for Nuclear Research (Geneva, Switzerland) ITER: International Thermonuclear Experimental Reactor (Gadarache, France) ISS: International Space Station (somewhere in sky..) Center of Ocean-Land- Atmosphere studies 72

IPCC AR4 Climate Modeling Centers UKMO FRCGC GISS NCAR MRI LASG MPI BCC BCCR CCCma CNRM MIUB IPSL CSIRO INMCM KMA GFDL 73

Scientific/Political Domains of Climate Modeling Facilities American node European/African node Asian/Australian node 74

Scientific/Political Domains of Climate Modeling Facilities American node European/African node Asian/Australian node UKMO FRCGC GISS NCAR MRI LASG MPI BCC BCCR CCCma CNRM MIUB IPSL CSIRO INMCM KMA GFDL 75

Summary 1.Models that better simulate the present climate produce the highest values of global warming for the 21 st century. 2.Models with low fidelity in simulating climate statistics have low skill in predicting climate anomalies. 3.Revolution in climate prediction is necessary and possible. 4.Seamless Prediction: From cyclone resolving global models to cloud system resolving global models 5.International collaboration is essential for: capacity building; model development; computational power Center of Ocean-Land- Atmosphere studies 76

Center of Ocean-Land- Atmosphere studies How to Implement a Seamless Prediction System in the midst of Several Pre-existing Separate, Independent National Centers for Weather, Climate, and Earth System Science? 77

US Climate Modeling Infrastructure and World Climate Computing Facility The US has 4 major climate modeling groups –GFDL, GISS, GSFC, NCAR (Three contribute to IPCC) The US has 2 major data assimilations/reanalysis groups –GSFC, NCEP (both in Washington, DC area) The US has multiple small groups that utilize climate models –COLA, CSU, FSU, IRI, MIT, UCLA, UH, UMCP…. The US has a large number of individual researchers (with students, post-doc’s, etc.) utilizing climate models and/or model outputs for research- too many to list.  How will the US participate in a World Climate Facility? Center of Ocean-Land- Atmosphere studies 78

Suggestions for India (may apply to Brazil) 1.The total national capacity for model development is limited: must build additional capacity Must use the existing capacity wisely Foster collaboration among groups (challenging!!) 2.Ensure that the (planned) climate center uses the operational NWP model as the dynamical core of the climate-earth system model (may require large effort; will foster national collaboration) 3.Develop mechanisms for perpetual interaction among groups for mutual benefit to keep the integrity of at least one truly unified national model development effort. Center of Ocean-Land- Atmosphere studies 79

Center of Ocean-Land- Atmosphere studies How to Implement a Seamless Prediction System in a “Free” Country (viz USA) 1.A Climate center (e.g. NCAR, GFDL) takes an operational NWP/DSP model as the dynamical core, and builds a complex climate-earth system model (energy balance; biogeochemistry etc.) (UNLIKELY) 2. An Operational NWP/DSP center (e.g. NCEP) takes the dynamical core of a climate model. (Note: The approximate transition periods for 1 and 2 is about 2 years) (UNLIKELY) 3. An Operational NWP/DSP center gradually expands its effort/ excellence towards NWP-Climate-Earth System Prediction System (IS IT POSSIBLE IN USA?) 80

Center of Ocean-Land- Atmosphere studies How to Implement a Seamless Prediction System in a “Free” Country (viz USA) 4. Create one new national (govt.; academia) “model development group” consisting of current and new staff that builds a new generation of weather-climate-earth system prediction system with complexity/ resolution/ensemble size unaffordable at individual centers. The current centers and research groups, universities use the model(s) for different applications (NWP, DSP, IPCC, mechanistic experiments etc.). 81

THANK YOU! Center of Ocean-Land- Atmosphere studies 82