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9 RECOMMENDATIONS Over the past decade we have learned much about the complex natural processes that influence climate variability and change, and our ability to model climate has increased significantly. We have gained a better appreciation for the important connections between physical, biological, and human dimensions of the climate system. We have also begun to better identify those parts of the climate system that are particularly important and not well understood, and that therefore limit our ability to project the future evolution of Earth's climate. A critical area where understanding is needed is the role of feedbacks in the climate system and their role in determining climate sensitivity. This Panel believes that refining our understanding of the key climate feedback processes and improving their treatment in models used to project future climate scenarios is an effective way forward in the quest to better understand how climate may evolve in response to natural and human- induced forcings. An appropriate strategy for accomplishing this is to make more rigorous comparisons of models with data and to focus particularly on observational tests of how well models simulate key feedback processes. This report highlights broad guidance on the key avenues of research that need to be pursued to better understand climate feedbacks and is intended to call attention to those areas where additional focus might be productive in the near term. The key finding of this report is that an enhanced research effort is needed to better observe, understand, and model key climate feedback processes. Research on climate feedback processes should be designed to Integrate observational and modeling efforts toward understanding and modeling of climate feedback processes; Integrate the subdisciplines of climate science for a comprehensive study of the key climate feedback processes; and 110

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RECOMMENDA TIONS 111 Integrate different time scales of weather and climate variability into studies of climate feedback processes. KEY OBSERVATIONS NEEDED TO MONITOR AND UNDERSTAND CLIMATE FEEDBACKS Because climate feedback processes are so important to understanding climate change, it is necessary to monitor the variables that characterize the feedback processes as well as the variables that define the basic climate. In addition to temperature and precipitation, variables such as clouds, water vapor, aerosols, land surface properties, snow cover, sea ice, and radiation budget quantities need to be monitored. Stable long-term measurements of these variables can be used to monitor the feedback processes, to better understand these processes, to identify the contributing causes to observed climate changes, and to improve confidence in climate projections. Recommendation: An integrated global climate-monitoring system must include observations of key climate feedback processes. Stable, accurate, long- term measurements should be made of the variables that characterize climate feedback processes. ~ As climate and greenhouse gases change, certain variables (see below) must be adequately monitored to advance the objectives of climate change feedbacks research and to define the state of the climate system including feedback processes. Although some of these observations are made, there are deficiencies. Some of these observations are made across insufficient lengths of time or across too limited regions, or are not made routinely and globally as required for global climate monitoring. Other measurements are made globally, but lack the quality required for long-term climate monitoring and analysis (see NRC, 1 999a, 2000b). 1. Observations with Insufficient Time or Space Resolution Ice thickness; Temperature and salinity of the upper ocean and other portions of the ocean that affect interannual to decadal climate change; Atmospheric trace gas concentrations (e.g., CO2, O2-N2, CH4, 03) and ocean chemistry; and Soil moisture profiles and snow properties (e.g., depth, moisture equivalent, snow state).

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112 UNDERSTANDING CLING TE CHANGE FEEDBACKS 2. Observations That Do Not Meet Quality Standards Temperature and humidity (particularly in the upper troposphere and stratosphere), precipitation, and wind, Global cloud and aerosol distributions and properties; Sea ice margin characteristics; Terrestrial vegetation, and snow extent; Radiation budget at the top-of-tropopause and at the surface; and Ocean color. The collection and validation of these datasets will require international collaboration and cooperation among U.S. agencies. As recommended in several previous NRC reports, there are advantages to collecting these observations in the context of an integrated global monitoring system (e.g., NRC, 1999a). Such a system is required for other aspects of climate change research and applications not addressed in this report including climate change attribution and detection and providing a broad range of climate and weather services (NRC, 2001 e). Details of these observation needs were presented in Chapters 2 through 8. As explained in Chapter 2, a water vapor observing system is needed that has sufficient accuracy to measure decadal trends in the water vapor distribution and sufficient spatial resolution to aid in understanding the mechanisms by which the water vapor distribution is maintained. The water vapor observing system should be closely linked to a global cloud, aerosol, and precipitation observing system. As was discussed in Chapter 3, detailed datasets for Arctic and Antarctic ice cover and albedo, and more comprehensive sea-ice thickness data are needed that extend over long periods of time to account for interannual variability. Techniques for efficiently measuring sea-ice thickness over the globe need to be developed. Improved definition of the basic temperature and salinity state of the upper ocean also is needed, as explored in Chapter 4. This will require full implementation of a system with the capabilities of the Argo global array of profiling floats, plus a strategy for monitoring key regions of the ocean that are important for the thermohaline circulation, such as the Labrador, Greenland-Iceland-Norwegian, Weddell, and Ross seas. The need for integrated datasets for soil moisture, skin and soil temperature, vegetation properties and cover, and snow water equivalent are examined in Chapter 5. A new suite of in situ and remotely sensed observations are needed to provide information on a variety of aerosol characteristics and key ~ As defined in NRC (1999a, 2000b).

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RECOMMENDA TIONS 113 atmospheric chemical properties that have been unavailable heretofore on a temporally or spatially extensive basis. These characteristics, outlined in Chapter 6, include the chemical composition of aerosol particles, optical extinction, and scattering under a wide range of conditions. Chapter 7 notes that a highly diverse set of observations is required to more tightly constrain understanding of Earth's biogeochemical feedbacks. These observations include O2-N2 ratio, ocean carbon in a variety of forms, ocean color, and atmospheric CO2 and CH4 concentration. EVALUATING PROGRESS IN UNDERSTANDING CLIMATE FEEDBACKS The simulation of individual feedback processes must be tested against appropriate observations in order to measure our understanding, reveal the reasons why climate sensitivity varies from model to model, and know which models are most likely to be correct. To do this requires a well- designed set of observed diagnostic tests, or metrics, that will measure our understanding and provide standards against which models can be tested. A sufficiently discriminating set of observational tests would lead to improvements in individual models and better methods for objectively rating the performance of climate models. Recommendation: Both global and regional metrics that focus on feedback processes responsible for climate sensitivity should be used to more rigorously test understanding of feedback processes and their simulation in climate models. An expanded set of data comparisons, or climate model performance metrics, should be developed that focus on each of the key climate feedback processes and include geographic and seasonal variations. In this context metrics are robust statistics that can be derived from observations and capture the essence of some fundamental aspect of a phenomenon or process. A first step toward developing metrics to evaluate feedback processes would be for the relevant agencies to organize a workshop or series of workshops to define observational metrics. These workshops would include scientists engaged in observation, diagnosis, and modeling of climate and climate processing. Effective metrics must be based on a basic description and at least a rudimentary understanding of the phenomenon or process in question. They

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114 UNDERSTANDING CLIMA TE CHANGE FEEDBA CKS must be tailored to the variety of time and space scales on which the relevant climate change feedbacks operate. They must be based on measurements that are of good quality and can define the process or phenomenon well. Metrics should evolve as our understanding and observations improve. A good set of diagnostic tests, or metrics, will do much more than assess the state of a system; it will also capture the co-variation or coupling between the system's components. If effectively employed, metrics can be an essential tool to help organize and stratify diagnostic analyses, as well as to relate model simulations to the fundamental aspects of observed phenomena. They can also be a useful tool to monitor the evolution of the climate system and thus make important contributions to the field of climate change detection and attribution. Utilization of observational metrics as proposed in this report would increase the observational constraints on feedback processes and could help to improve confidence in regional climate projections. A broadly accepted suite of climate feedbacks metrics could also provide a partial solution to the "need for uniform criteria with which to judge climate models," which has been identified as a key issue in a previous NRC report ARC, 2001c). Examples of Metrics Because the magnitude of the changes in feedback processes may not yet have been sufficiently large to evaluate our understanding of the feedbacks as they relate to long-term climate change, it is not possible to fully evaluate an understanding of climate feedback processes by observing long-term trends in global climate. An alternative strategy is to use observations of shorter-term variability to test understanding and simulation of climate feedback processes. Strongly forced variations such as annual, diurnal, and ENSO cycles seem to provide valuable tests for understanding the processes underlying climate change feedbacks. Although there are benefits to using short-term variability as a diagnostic tool for improving understanding and modeling of climate feedback processes, it is recognized that success in simulating the role of climate feedback processes in short-term climate variations does not necessarily translate to success in simulating global warming. Nevertheless, short-term climate variability provides a promising avenue for quantitatively evaluating the feedback processes of a model used for the study of global warming. In other words, a model may not be regarded as reliable for climate change projections simply because it realistically simulates feedbacks on short time scales. But if a climate model can accurately

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RECOMMENDA TIONS 115 simulate climate feedback processes on interannual, annual, and shorter time scales, a much stronger argument can be made that it should be reliable for climate change projections. An example of a large-scale regional metric is the response of atmospheric temperature, water vapor, clouds, radiation fluxes, and atmospheric circulation to sea surface temperature anomalies associated with warm events in the tropical Pacific (e.g., Hartmann and Michelsen, 1993, Ramanathan and Collins, 1991~. This approach can be expanded to examine the covariability of sea surface temperature, tropical convection, upper tropospheric water vapor, the vertical profile of atmospheric temperature, and other observations over a variety of time scales, including the seasonal time scale. These covariance metrics should then be applied to model simulations to pinpoint those aspects of the models that appear to accurately represent nature and those that require further work. On the continents the global warming response of precipitation, clouds, and soil moisture is important. A metric that might enable improvement of feedback processes over land would be the simulation of the observed diurnal variations of temperature, clouds, and precipitation, and the slow evolution of the diurnal cycle on the seasonal timescale as, for example, soil moisture decreases during the summer months. The snow and ice feedback could be quantified by linking the regional climate sensitivity and the amplitude of the seasonal cycle to the snow cover and surface energy balance for latitude-longitude blocks of the North American and Eurasian continents. Many other possible metrics for testing the simulation of climate system feedbacks can be envisioned. Individual disciplinary chapters in this report give additional examples of metrics that might be used to diagnose specific aspects of climate system feedbacks. The set of metrics will likely evolve with time as understanding and simulation of the climate system evolves and improves. Climate Modeling and Analysis for Climate Feedbacks Research A practical goal of climate feedbacks research is to provide information necessary to support more reliable projections of future climates. To test understanding and modeling of climate feedback processes using a set of climate feedback metrics requires a substantial infrastructure and a proportionate intellectual effort. To undertake a rigorous program of testing the simulation of climate feedback processes in our most capable climate models requires that the observations and the expertise in applying them be

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116 UNDERSTANDING CLIMATE CHANGE FEEDBACKS brought together with the modeling capability. Previous NRC reports have stated the need for capable and effective climate modeling facilities (NRC 1998c, 2001 c), and have recommended the development of centralized operations for climate predictions and ozone assessments (NRC, 2001 c). To advance understanding of climate change feedbacks and their role in climate sensitivity it is essential that U.S. climate modeling facilities also have the capability and mandate to test climate feedback processes and their interactions using the most discriminating observational constraints. Within the context of climate feedback processes this will also address the need for uniform criteria with which to judge climate models (NRC, 2001 c). The Panel on Improving the Effectiveness of U.S. Climate Modeling has previously noted "the need for strong interaction between observations of the climate system, research into fundamental climate processes, and integrative climate modeling" (NRC, 2001 c). That same panel recommended enhanced resources for centralized operational activities addressed to short- term climate predictions, to the study of predictability of climate on decadal and century time scales, and to assessments of ozone depletion and climate change (NRC, 2001 c). A research program that uses observable metrics to test our understanding and simulation of climate change feedbacks should be applied most assiduously to the models that are most capable of both simulating the complex interactions of the various feedback processes and making climate change projections for planning purposes. Applying the most stringent observational constraints and tests to the most capable integrated models will benefit climate feedbacks research and increase our confidence in the climate projections made with these models. For this reason it is important that the research and operational facilities operating the most capable climate models have access to the data and expertise necessary to employ the most discriminating metrics of the feedback processes. Recommendation: Climate modeling facilities in the United States must be given the capability and mandate to test understanding and simulation of climate feedback processes and their interactions using the best observational constraints on climate feedback processes. Periodic assessment of the progress being made by major climate models should be conducted to evaluate the ability of these models to simulate the processes underlying key climate system feedbacks. Testing and development of climate models can make more effective use of existing datasets, and should rapidly incorporate new datasets as they

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RECOMMENDA TIONS 117 become available. It is important that these efforts focus more directly on issues related to the specific testing and quantification of feedback mechanisms in climate models. The Atmospheric Model Intercomparison Project (AMIP) and the Coupled Model Intercomparison Project (CMIP) have been very useful for evaluating the performance of GCMs in simulating the geographical distribution of climate and its seasonal variation (e.g. Covey et al., in press; Gates et al., 1998~. These analyses indicate relatively good matches between some aspects of the observed climate and the climate produced by state-of- the-art GCMs. It is highly desirable to develop a specific methodology for the improvement and quantitative assessment of modeled feedback processes and their effect on climate sensitivity. The approach recommended here is a more specific focus on comparing observed measures of climate feedback processes with the same measures produced by climate models. Representations of critical climate processes in climate models are becoming more realistic and must be rigorously tested against all available observations. These efforts should remain focused on the objective of producing more robust model representations of nature, and be prioritized according to their impact on projections of future climates. One approach for facilitating model improvements of key processes is the Climate Process Team (CPT) concept, currently being developed by the U.S. CLIVAR program. It has the potential to foster more comprehensive investigations of climate change feedbacks. In this approach, teams of scientists, including observationalists, process modelers, and global climate modelers, are to undertake relatively comprehensive and integrated projects focused on specific climate feedback processes and their treatment in climate models. CPTs have the potential to make significant progress toward reducing and better characterizing uncertainty associated with climate change feedbacks, provided that they can develop and maintain a sharp focus on the objective of improving model representations of the key processes.