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Seamless Weather and Climate Prediction

Climate Test Bed Seminar Series 10 February 2009. Seamless Weather and Climate Prediction. Jagadish Shukla George Mason University (GMU), USA Institute of Global Environment and Society (IGES). Center of Ocean-Land-Atmosphere studies.

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Seamless Weather and Climate Prediction

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

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

  3. 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

  4. CLIVAR 1995  CliC 2000 GEWEX 1988  SOLAS 2001 -> SPARC 1992 WGNE WGCM WGSF WCRP Observation & Assmilation Panel WCRP Modelling Panel Coordinated Observation and Prediction of the Earth System Center of Ocean-Land-Atmosphere studies

  5. The WCRP Strategic Framework 2005-15 Coordinated Observation and Prediction of the Earth System (WCRP-COPES) 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 Center of Ocean-Land-Atmosphere studies

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

  7. WCRP Strategic Framework (COPES) Seamless Prediction Problem • 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

  8. WCRP Strategic Framework (COPES) 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

  9. 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

  10. 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

  11. Some Examples of Seamless Processes Tropical Convection (SST) Rossby Waves (Atmosphere) North America Forest Fires (Land) 1. Surface Wind (Atmosphere) Persistent Drought (Land) Influence ENSO (Ocean) 2. Eurasian Snow (Cryosphere) Wet/Dry soil, Ts (Land) Monson Droughts (Atmosphere) 3. Pacific/IO SST (Ocean) Walker cell (Atmosphere) Asian Monsoon (Land) 4. Upp. Stratosphere Circ. Propagation down Extra Trop. Surface Winds 5. Center of Ocean-Land-Atmosphere studies ISO/MJO ENSO Global Warming 6. Global change Regional SSTA Hurricanes 7.

  12. Seamless Prediction of Weather and Climate From Cyclone Resolving Global ModelstoCloud System Resolving Global Models • Planetary Scale Resolving Models (1970~):Δx~500Km • Cyclone Resolving Models (1980~):Δx~100-300Km • Mesoscale Resolving Models (1990~):Δx~10-30Km • Cloud System Resolving Models (2000 ~):Δx~3-5Km Center of Ocean-Land-Atmosphere studies Mesoscale System Organized Convection Cloud System Synoptic Scale Planetary Scale Convective Heating MJO ENSO Climate Change

  13. WMP reports to JSC (Zanzibar, 2007) that climate models have serious problems 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 Center of Ocean-Land-Atmosphere studies

  14. Systematic Error: MSLP (NDJ)

  15. Infamous Double ITCZ Problem

  16. Annual Cycle of SST Climatology 4-6 month forecast, APCC/CliPAS & DEMETER CGCMs Calendar Month Center of Ocean-Land-Atmosphere studies Jin et al. 2008 Climate Dynamics

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

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

  19. Influence of Systematic Error on CFS Forecast Skill CORR. with respect to lead month based on 1st SEOF mode of SST NINO3: Warm minus Cold composite Correlation SST anomalies Forecast lead month (Hindcast composite) Correlation between 1st PCs based on observation andhindcasts at different lead times Correlation between 1st PCs based on long run and hindcasts at different lead times Observation CFS long run • 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 Jin and Kinter 2009, Climate Dynamics  Model Flaw: Slow coupled dynamics of CGCM

  20. 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. Center of Ocean-Land-Atmosphere studies (Moncrieff, Shapiro, Slingo, Molteni, 2007)

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

  22. Rainfall 1982-83 1988-89 Zonal Wind 1982-83 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). Center of Ocean-Land-Atmosphere studies 1988-89

  23. Evolution of Climate Models1980-2000 Model-simulated and observed, 1983 minus 1989 Rainfall(mm day-1) 500 hPa GPH anomaly(m) KUO R15 RAS R40 Center of Ocean-Land-Atmosphere studies Observed

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

  25. Evolution of Climate Models 1980-2000 Model-simulated and observed rainfall anomaly (mm day-1) 1983 minus 1989 Center of Ocean-Land-Atmosphere studies

  26. Evolution of Climate Models 1980-2000 Model-simulated and observed 500 hPa height anomaly (m) 1983 minus 1989

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

  28. Center of Ocean-Land-Atmosphere studies MRF8: high, middle, low clouds allowed to exist MRF9: Only high cloud allowed to exist over regions of tropical deep convection Kumar et al. 1996 Journal of Climate

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

  30. Vintage 2000 AGCM

  31. Annually & Zonally Averaged Reflected SW Radiation Center of Ocean-Land-Atmosphere studies Bjorn Stevens, UCLA World Modelling Summit, ECMWF, May 2008

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

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

  34. Conjectures 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. Center of Ocean-Land-Atmosphere studies

  35. Climate Model Fidelity and Projections of Climate Change J. Shukla, T. DelSole, M. Fennessy, J. Kinter and D. Paolino Geophys. Research Letters, 33, doi10.1029/2005GL025579, 2006 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 CO2. 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.

  36. Climate Model Fidelity and Projections of Climate Change • Relative Entropy: The relative entropy between two distributions, p1(x) and p2(x), is defined as • (1) • where the integral is a multiple integral over the range of the M-dimensional vector x. • (2) • where jk is the mean of pj(x) in the kth season, representing the annual cycle, j is the covariance matrix of pj(x), assumed independent of season and based on seasonal anomalies. The distribution of observed temperature is appropriately identified with p1, and the distribution of model simulated temperature with p2. 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. Conjectures 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. Center of Ocean-Land-Atmosphere studies

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

  41. Examples of SuccessUnderstanding 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. Center of Ocean-Land-Atmosphere studies

  43. Time series VW 850 tropics

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

  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 2006

  47. Nastrom & Gage,1985

  48. k-3 dx= 40km 25km 10km k-5/3 Spectra of Total KE log10k

  49. Closest attempt to global cloud resolving model so far … Center of Ocean-Land-Atmosphere studies • Ocean-covered Earth • Geodesic grid • 3.5 km cell size, ~107 columns • 54 layers, top at 40 km • 15-second time step • ~ 1 TF-day per simulated day Running on Earth Simulator Masaki Satoh, Hirofumi Tomita, Hiroaki Miura, Shinichi Iga and Tomoe Nasuno, 2005: J. Earth Simulator, 3, 1-9.

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