1. Briefing for CCSP Observations Working Group May 10, 2004 Model-Observations Integration Randall Dole, NOAA, CCSP Co-lead Climate Variability and Change (CVC) and Climate Modeling (CM) Working Groups Why is this integration essential to a climate observing strategy? Role in the CCSP Strategic Plan? CCSP Synthesis and Assessment Product: Reanalysis/attribution. Science and technical challenges. Some key issues. Intro.

2. Why is this integration essential? Integrating diverse observations into a physically- based model through the process of data assimilation is vital for constructing near real-time climate analyses and periodic reanalyses of past climate.  Modern data assimilation techniques enable data from many disparate observing systems to be effectively integrated together within a model to form a consistent climate system analysis.  Climate analyses and reanalyses have numerous climate applications. They directly support research to: advance understanding and predictions of climate, diagnose deficiencies in climate models, clarify observing system requirements and data set needs, and develop decision support resources. Climate analyses that are obtained by assimilating observations into a state-of-the art model are an essential component of an end-to-end climate observing system. Why?

3. Role in the CCSP The fundamental need for ongoing, near real-time climate analyses, together with periodically updated climate reanalyses, is well recognized in the CCSP SP. This need is not new. It has been articulated repeatedly by the scientific community and science advisory panels for over a decade. Specific CCSP goals, questions, objectives, research foci, and products directly connected to or dependent on climate analyses are listed in: Ch 2. Integrating Climate and Global Change Research Ch 4. Climate Variability and Change. Ch 10. Modeling Strategy. Ch 11. Decision Support Resources Development. Ch 12. Observing and Monitoring the Climate System. Ch 13 Data Management and Information. Climate analyses in the CCSP

4. Why is climate analysis/reanalysis such a high “observational” priority for CVC/CM WGs? Climate analyses derived from data assimilation integrate observational data with climate models. They can be used to evaluate model deficiencies and identify areas where improvements in models and observational data may be particularly beneficial. They are virtually essential for many diagnostic studies, as well as S-I prediction research. Climate analyses derived from data assimilation integrate observational data with climate models. They can be used to evaluate model deficiencies and identify areas where improvements in models and observational data may be particularly beneficial. They are virtually essential for many diagnostic studies, as well as S-I prediction research. Ongoing climate analyses and updated reanalyses would support both CVC and CM needs more than any single observational data set. Ongoing climate analyses and updated reanalyses would support both CVC and CM needs more than any single observational data set. This integrating activity would help to address a broader array of questions and likely provide a higher return-on- investment than any single observational data set. This integrating activity would help to address a broader array of questions and likely provide a higher return-on- investment than any single observational data set. Climate analysis and reanalysis products serve a very broad community, including scientists and end users. Climate analysis and reanalysis products serve a very broad community, including scientists and end users.

5. CCSP Synthesis and Assessment Product Product: Reanalysis of historical climate data for key atmospheric features. Implications for attribution of causes of observed change. Significance: Understanding the magnitude of past climate variations is key to increasing confidence in the understanding of how and why climate has changed and how it may change in the future. Significance: Understanding the magnitude of past climate variations is key to increasing confidence in the understanding of how and why climate has changed and how it may change in the future. Primary end use: To inform policy decisions. Primary end use: To inform policy decisions. Proposed lead agencies: NOAA, NASA, DOE supporting. Proposed lead agencies: NOAA, NASA, DOE supporting. CCSP WGs: Initially assigned to OWG, but CVC and CM WG have a strong interest and have been involved in early science planning. CCSP WGs: Initially assigned to OWG, but CVC and CM WG have a strong interest and have been involved in early science planning. Time frame: 2-4 years. Time frame: 2-4 years.

6. CCSP Reanalysis Synthesis Product: Update on Progress Interagency Science Working Group has been meeting since December. Interagency Science Working Group has been meeting since December. Co-chairs: Siegfried Schubert (NASA), Glenn White (NOAA). Co-chairs: Siegfried Schubert (NASA), Glenn White (NOAA). Approximately 20 participants, from NOAA, NASA, DOE, NCAR, University community. Approximately 20 participants, from NOAA, NASA, DOE, NCAR, University community. Draft plan developed for proposed products. Draft plan developed for proposed products.

7. Proposed Products A. State-of-science science reviews 1. Assessment of first generation reanalysis products 2. Assessment of current understanding of causes of 20 th century climate variability and trends century climate variability and trends B.Develop high quality observational datasets. 1. For satellite era, correct sat. biases/trends, land surface, ocean data sets required for coupled data assimilation. 2. For pre-radiosonde era, QC surface press. obs crucial. C.Initiate next generation reanalyses Three proposed activity streams: 1. Satellite era (~1979 to present) 1. Satellite era (~1979 to present) 2. Period with substantial upper air network (~ post-1948) 2. Period with substantial upper air network (~ post-1948) 3. Period with minimum set of surface obs (~1895 to present) 3. Period with minimum set of surface obs (~1895 to present) Proposed primary products

8. Synthesis and Assessment Reports Proposed topics 1) State-of-science assessment of strengths and weaknesses of first generation reanalyses, their suitability and limitations for studies of climate variability and trends. 2)State-of-science assessment of present knowledge of understanding and uncertainties of causes of observed climate variations and trends during the 20th century. Possible additional report, or in 1): 3) Assessment of progress since first generation reanalysis to improve climate analyses and reanalyses, key issues, and necessary further steps to address outstanding science and policy-relevant questions. Proposed SARs

9. Scientific and Technical Challenges  Data inhomogeneities in space and time.  Model biases.  Optimizing analyses for climate purposes.  Optimal use of data, minimization of spurious trends, bias, etc.  Improved representation of processes and forcing, e.g., precipitation, clouds, interactions with surface.  Better horizontal and vertical resolution.  Estimating uncertainties.  Use of data assimilation to extend reanalysis back in time.

10. Example of use of modern data assimilation methods to integrate observations with a model. Feasibility of a pre-radiosonde era reanalysis Analysis of pre-radiosonde era  Current analyses for the pre-radiosonde (ca. 1948) period consist of subjectively produced hand-drawn SLP maps that did not use all available observations. Can modern data assimilation systems be used to improve on these analyses? Can this approach provide us with additional information on the large-scale tropospheric anomalies, e.g., during the dust bowl years?  A feasibility study was conducted using data removal and ensemble data assimilation techniques. Simulated reanalyses use only surface pressure observations at densities and temporal intervals representative of earlier years (1895, 1915, 1935). Major science/policy-relevant question: Can we better describe and interpret the causes of past climate variability and change over the last 100-150 years?

11. 500mb Height Analyses for 0Z 15 Dec 2001 Full CDAS (120K+obs) EnSRF 1895 (214 surface pressure obs) OI 1895 (214 surface pressure obs) 5500 m contour is thickened Black dots show pressure observation locations RMS = 39.8 m r (z’,NH)= 0.96 RMS = 82.4 m

12. Results indicate that: 1.Reanalyses of the lower-tropospheric circulation prior to 1948 are feasible using just the available surface pressure observations. 2.Recent advances in ensemble data assimilation methods may lead to even better analyses, including for the upper troposphere. 3.Providing additional observations, especially in data sparse regions, will produce further improvement. Present approaches and data coverage should enable lower tropospheric reanalyses that are as accurate as current 2-3 day forecasts. 4.Having the most complete and carefully quality-controlled surface pressure data sets available will be especially crucial for historical reanalyses.

13. Some key issues Leadership. Which Agency(ies)? Which WG(s)? Leadership. Which Agency(ies)? Which WG(s)? Short-term deliverables and data set development. What else could or should be accomplished before FY08? Needs differ for three streams of reanalyses, and for ongoing climate analyses. Short-term deliverables and data set development. What else could or should be accomplished before FY08? Needs differ for three streams of reanalyses, and for ongoing climate analyses. Support for data assimilation research, integration of observational and modeling capabilities. Support for data assimilation research, integration of observational and modeling capabilities. Roles and responsibilities. Coordination in CCSP, across agencies, and with extramural community. Roles and responsibilities. Coordination in CCSP, across agencies, and with extramural community. International coordination. Linkage to EOS/GEO. International coordination. Linkage to EOS/GEO.

14. Concluding comments A complete climate observing system requires both ongoing, near-real time climate analyses and periodic reanalyses using improved data sets and data assimilation methods. Both must be considered as essential components of a long- term climate observing strategy.A complete climate observing system requires both ongoing, near-real time climate analyses and periodic reanalyses using improved data sets and data assimilation methods. Both must be considered as essential components of a long- term climate observing strategy. So far, climate analyses and reanalyses have focused on the atmosphere. A longer-term strategy must be developed to analyze and eventually bring together the other, disparate components of the Earth system (oceans, land, cryosphere, hydrology, biosphere) through coupled model assimilation. This will enable a more comprehensive synthesis and understanding of the climate system.So far, climate analyses and reanalyses have focused on the atmosphere. A longer-term strategy must be developed to analyze and eventually bring together the other, disparate components of the Earth system (oceans, land, cryosphere, hydrology, biosphere) through coupled model assimilation. This will enable a more comprehensive synthesis and understanding of the climate system.