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NCAR Initiative on Weather and Climate Assessment Science Linda O. Mearns Doug Nychka and Jerry Meehl (acting co-directors) Overarching Goals of Assessment Initiative • Develop new programs that address research gaps in Impact Assessment Science and that leverage expertise at NCAR. • Emphasize themes that integrate research across NCAR divisions. • Foster NCAR’s leadership role in the IPCC and other national and international efforts related to assessment. • Create feedback between the impact and assessment communities and the geophysical modeling programs Main Initiative Themes • Characterizing Uncertainty in Assessment Work • Modeling and Assessment of Extreme Events • Establishment of a Climate/Human Health Program Initiative Management L. Mearns, Director G.Meehl and D. Nychka Acting Co-directors Advisory Board Initiative Representation B. Harriss W. Washington V. Holzhauer Administrator Mentors G. Bonan, B. Brown, R. Katz, K. Miller, R. Morss, T. Wigley Cyber/infrastructure, Biogeosciences, Data Assimilation, GIS, Water Cycle, Wildfire. Characterizing Uncertainty in Assessment Work “To know one’s ignorance is the best part of knowledge” - Lao Tzu “Doubt is not a pleasant condition, but certainty is an absurd one” -Voltaire Projections of Future Climate Uncertainty due to spatial scale T ??P T ?P T P Temperature Precipitation Total Cumulative Carbon Dioxide Emissions (GtC) 3000 2500 A1F1 2000 High > 1800 GtC A2 Medium High 1450-1800 GtC A1B 1500 Medium Low 1100-1450 GtC 1000 B2 A1T Low < 1100 GtC B1 500 0 1990 IS92 Range 2000 2010 2020 2030 2040 2050 2060 2070 Cumulative Emission 1990-2100, CtC 2080 2090 2100 A Cascade of Uncertainty for Climate Change Research Characterizing Uncertainty in Assessment Work • Climate projections and scenarios • Emissions and land processes • Impacts models (e.g., agriculture and ecosystem models) • Environmental data sets (e.g., climate observations, climate proxy data, soils) • Uncertainty and decision making Uncertainty and Decision Making (some details) • Role of different types of uncertainty in different phases of the policy decision process. • Uncertainty and multiple decision makers in resource management. • Understanding policy makers' needs for quantification of uncertainty and adapting the analysis of climate projections and scenarios to address these needs. • Economic value of reducing uncertainty in weather and climate information. Extreme Events “Man can believe the impossible. But man can never believe the improbable.” - Oscar Wilde Fort Collins Flood, July 1997 Heaviest rains ever documented over an urbanized area in Colorado (10 inches in 6 hours). 5 dead, 54 injured, 200 homes destroyed, 1,500 structures damaged. These locations were not in 500-yr floodplain. Weather and Climate Extremes Atmospheric Processes Modeling of Extremes Extremes toolkit Trends in Observations Weather Impacts and Vulnerabilities Extreme Value Methodology Climate Change Extreme Events Integrate different aspects of research in weather and climate extremes: • Atmospheric science (processes and modeling) • Statistical aspects of extremes • Societal impacts and vulnerability Atmospheric Processes and Modeling of Extremes Modeling of extremes Regional climate and mesoscale model validation how well models reproduce extremes. Spatial scaling of extremes - point versus area average. Projections of changes in extremes with climate change. Research in the physical processes of extremes (e.g., warm season heavy rainfall – requires partnership with Water Cycle Initiative) Application of Extreme Value Methodology Normal Max 100 Normals • Analysis of weather and climate variables in terms of tail events and their properties at different spatial scales. • Trend analysis of extremes (e.g., temperature and precipitation). • Spatial dependence of extreme events. Societal Impacts of and Vulnerability to Extremes • Identification of extremes significant to society • Modeling the impacts of extreme events • Tools to reduce societal vulnerability to extremes Understanding vulnerability requires knowledge of the behavior and interactions of all systems involved in an extreme event e.g., town economics storm meteorology flood hydrology Climate/Health Program Interdisciplinary Community Integrated Approach Physical Data Rescue and Archive Biological • Hydrology • Oceanograph y • Climatology Program Vulnerability and Risk Assessment Analytical Studies Social • Public Health • Demography • Economics • Sociology • • • • Design Analysis Ecology Entomology Microbiology Mammalogy Modeling and Prediction Observation Monitoring Surveillance Initiative Highlights Uncertainty • Uncertainty due to land cover changes • Sensitivity and scaling of climate model results • Combining multi-model ensembles Extremes • Change in frost days in climate projections • 24-hour precipitation extremes and flood planning (also a use of the Extremes Toolkit) Land Cover Forcing from SRES Scenarios in Climate Models How do changes in land use and land cover alter climate projections? NCAR Team: G. Bonan, L. Mearns, J. Meehl, K. Oleson External Collaborators: J. Feddema, U. of Kansas, R. Leemans, M. Schaeffer, RIVM, Netherlands Potential (or2.2Natural) Vegetation IMAGE - 1970 Potential Vegetation Impact of Croplands on Climate IMAGE 2.2 - 1970Land Land Cover IMAGE 1970 Cover 5 - Regrowth (tim b er) 11 - Tem p erate M ix ed F ores t 17 - S avann a 0 - Ocean 6 - Ice 12 - Tem p erate Decid Forest 18 - Trop ical W ood lan d 1 - Agri cult ure 7 - Tund ra 13 - W arm M ixe d Fo rest 19 - Trop ical Fores t 2 - Exte ns ive grass lan d 8 - Woo ded Tun dra 14 - G ras s/S tep pe No D ata 3 - C p lan tatio n - NU 9 - Bo real Fo re st 15 - D esert 4 - Reg ro wth (ab an do n) 10 - C ool C on ifer 16 - S crub land IMAGE 2.2 Land Cover Types Model Experiments • Multi-decadal climate model simulations with the Parallel Climate Model (Washington & Meehl) • Uses NCAR LSM as land surface model • One simulation with potential vegetation • Another with 1970 (present-day) land cover 5 - Regrowth (tim b er) 11 - Tem p erate M ix ed F ores t 17 - S avann a 0 - Ocean 6 - Ice 12 - Tem p erate Decid Forest 18 - Trop ical W ood lan d 1 - Agri cult ure 7 - Tund ra 13 - W arm M ixe d Fo rest 19 - Trop ical Fores t 2 - Exte ns ive grass lan d 8 - Woo ded Tun dra 14 - G ras s/S tep pe No D ata 3 - C p lan tatio n - NU 9 - Bo real Fo re st 15 - D esert 4 - Reg ro wth (ab an do n) 10 - C ool C on ifer 16 - S crub land IMAGE 2.2 Land Cover Types Climate Change From Present-Day Croplands Summer (JJA) Daily Maximum Temperature (40 Year Average) Present Day Land Cover – Natural Land Cover • Decreased daily maximum temperature in June-August of present-day croplands compared to natural vegetation • Due primarily to higher albedo of croplands, but also to changes in evapotranspiration Transient Climate Change Simulations IMAGE 2.2 - A2: 2100 Land Cover 5 - Regrowth (tim b er) 11 - Tem p erate M ix ed F ores t 17 - S avann a 0 - Ocean 6 - Ice 12 - Tem p erate Decid Forest 18 - Trop ical W ood lan d 1 - Agri cult ure 7 - Tund ra 13 - W arm M ixe d Fo rest 19 - Trop ical Fores t 2 - Exte ns ive grass lan d 8 - Woo ded Tun dra 14 - G ras s/S tep pe No D ata 3 - C p lan tatio n - NU 9 - Bo real Fo re st 15 - D esert 4 - Reg ro wth (ab an do n) 10 - C ool C on ifer 16 - S crub land IMAGE 2.2 Land Cover Types Currently In Progress: Transient climate simulations from 1870-2100 using historical and future land cover change Sensitivity of Climate Models to Natural and Anthropogenic Forcings • Paleo-climate simulation using PaleoCSM to test the validity over a long integration period • Designed suite of forcings to probe model sensitivity for the 20th century • Scaling of models to new forcings NCAR Team: C. Ammann, G. Meehl, C. Tebaldi, B. Otto-Bliesner, E. Wahl Outside Collaborators: P. Naveau (CU), N. Graham (Scripps/HRC), M. Mann (UVA), P. Jones (CRU-UK), H.-S. Oh (UAlberta), F. Joos, C. Casty, J. Luterbacher (UBern), J. Bradbury, R. Bradley (UMass), K. Cobb (CalTech) Model Validation with the Proxy Climate Record 20th Century Climate • Climate models with only “natural” forcings (volcanic and solar) do not reproduce observed late 20th century warming • When increases in anthropogenic greenhouse gases and sulfate aerosols are included, models reproduce observed late 20th century warming Years Scaling of Climate Models Pilot project uses PCM results from the B2 scenario and scales to the A2 scenario using a simple linear regression A22100 B21990 ( B22100 B21990 ) Here the regression coefficient is based on the global mean temperature estimated (usually by an energy balance model) for the A2 scenario. A key statistical challenge is to characterize the error in this method. Scaled spring precipitation field for A2 scenario Actual climate projection Error field scaled by natural variability Scaling Errors as a Function of Resolution Relative RMS errors for different grid box sizes 8 Realizations of Error Fields Along with the Actual Errors How do we combine the results of several climate models to make inferences about changes in regional climate ? NCAR Team: C. Tebaldi, D. Nychka, G. Meehl External Collaborators: R. Smith (UNC) Regional Statistical Analysis Regional Inference for Climate Change Circa the Third AssessmentA2 Report 9 AOGCM Projections Scenario Central Asia used as an example Combining Multi-Model Ensembles Consider the results of several models as a sample from a hypothetical super population of models: X j m ej Yj n e j j= 0: observed data j=1, M: the M models X: current climate Y: future projection m and n : true values The variance in the errors is determined based on principles of model bias and model convergence Posterior Distributions for Current and Future Winter Temperatures (DJF) for Central Asia °C Inference for the Mean Temperature Change °C Multivariate Model - Pooling Uncertainties °C Regions Extremes from Climate Model Projections Number of frost days within a year is a useful indicator for determining agricultural impacts and also is a more extreme measure of climate variability NCAR Team: C. Tebaldi, G. Meehl Model vs. Data: Changes in frost days in the late 20th century show biggest decreases over the western and southwestern U.S. in observations and the model Future changes in frost days from the climate model show greatest decreases in the western and southwestern U.S., similar to late 20th century Large-scale changes in atmospheric circulation affect regional pattern of changes in future frost days Anomalous ridge of high pressure brings warmer air to northwestern U.S. causing relatively less frost days compared to the northeastern U.S. where an anomalous trough brings colder air from north H cold L warm Influence of Climate Variability and Uncertainty on Flood Hazard Planning in Colorado • Extreme policy and decision making • Precipitation event analysis • Impacts of flood hazard planning Standard tool for assessing flooding hazards is the Colorado Precipitation-Frequency Atlas for the Western US, NOAA (1973), giving contours of rain rates for various return periods. The atlas has few measures of statistical uncertainty NCAR Team: M. Downton, M. Crandell, O. Wilhelmi, R. Morss, U. Schneider, E. Gilleland External Collaborators: P. Naveau (CU), R. Smith (UNC), A. Grady (NISS) Extremes Analysis of Boulder Daily Precipitation • Simplest analysis is fitting a generalized extreme value distribution to the annual maxima • An exceedance over threshold model can account for seasonality and other covariates • A common summary is the return time: e.g., the size of an event whose average time to occur is 100 years Analysis Using Extremes Toolkit Inference for the 100 Year Return Level for Boulder Integration Across Uncertainty • Decision making in resource management – water resources • Quantification of uncertainty in regional climate change projections • Climate/health issues American Water Works Research Foundation Collaboration with NCAR Project to Identify Impacts of Global Climate Change on Water Utilities (K. Miller and D. Yates). Use a “primer on climate change” and a workshop (Spring ’04) as vehicles to elicit feedback from managers on multi-model statistical summaries. These results will be used to modify the statistics. Global Regional Water Resources Assessment Sacramento WNA ΔTemp, DJF San Joaquin %PCP, DJF 30 CC scenar JJA C 20 hist 10 DJF 20000 mm CC scenar 0 %PCP, JJA 30 JJA 20 C 10 DJF 0 2000 1000 mm Statistical Downscale (Yates et al. 2003) ΔTemp, JJA hist 1000 0 1 11 21 31 41 51 61 71 81 91 Climate/Health • Climate/health issues integrate well with other parts of the Initiative • Both temperature and precipitation extremes are important contributors to problems in human health • Many important issues of uncertainty in attribution of climate as a cause of health problems (e.g., vector-borne disease) Management Monthly Meetings of Advisory Board – to discuss topics such as: • Web site development • Find and develop points of integration across projects • Promote integration with other initiatives