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

Characterizing Uncertainty in Assessment Work Modeling and Assessment of Extreme Events Establishment of a Climate/Human Health Program Main Initiative Themes

Initiative Management L. Mearns, Director B. Harriss G.Meehl and D. Nychka Acting Co-directors W. Washington Advisory Board Mentors V. Holzhauer Administrator G. Bonan, B. Brown, R. Katz, K. Miller, R. Morss, T. Wigley Initiative Representation 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

Uncertainty due to spatial scale TPTP T ?P T ??P Uncertainty Projections of Future Climate Temperature Precipitation

Total Cumulative Carbon Dioxide Emissions (GtC) Cumulative Emission , CtC High > 1800 GtC Medium High GtC Medium Low GtC Low < 1100 GtC A1F1 A2 A1B B2 A1T B1 IS92 Range

A Cascade of Uncertainty for Climate Change Research

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 Characterizing Uncertainty in Assessment Work

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. Uncertainty and Decision Making (some details)

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.

Atmospheric Processes Impacts and Vulnerabilities Extreme Value Methodology Trends in Observations Climate Change Weather Modeling of Extremes Extremes toolkit Weather and Climate Extremes

Integrate different aspects of research in weather and climate extremes: Atmospheric science (processes and modeling) Statistical aspects of extremes Societal impacts and vulnerability Extreme Events

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. Atmospheric Processes and Modeling of Extremes Research in the physical processes of extremes (e.g., warm season heavy rainfall – requires partnership with Water Cycle Initiative)

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. Application of Extreme Value Methodology Normal Max 100 Normals

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 storm flood economics meteorology hydrology Societal Impacts of and Vulnerability to Extremes

Climate/Health Program Interdisciplinary Community Integrated Approach Physical Hydrology Oceanography Climatology Social Public Health Demography Economics Sociology Biological Ecology Entomology Microbiology Mammalogy Observation Monitoring Surveillance Analytical Studies Modeling and Prediction Design Analysis Vulnerability and Risk Assessment Data Rescue and Archive Program

Initiative Highlights Uncertainty due to land cover changes Sensitivity and scaling of climate model results Combining multi-model ensembles Uncertainty

Change in frost days in climate projections 24-hour precipitation extremes and flood planning (also a use of the Extremes Toolkit) Extremes

Land Cover Forcing from SRES Scenarios in Climate Models NCAR Team: G. Bonan, L. Mearns, J. Meehl, K. Oleson External Collaborators: J. Feddema, U. of Kansas, R. Leemans, M. Schaeffer, RIVM, Netherlands How do changes in land use and land cover alter climate projections?

Impact of Croplands on Climate 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 Potential (or Natural) Vegetation IMAGE 1970 Land Cover

Climate Change From Present-Day Croplands 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 Present Day Land Cover – Natural Land Cover Summer (JJA) Daily Maximum Temperature (40 Year Average)

Transient Climate Change Simulations Currently In Progress: Transient climate simulations from using historical and future land cover change

Sensitivity of Climate Models to Natural and Anthropogenic Forcings Paleo-climate simulation using Paleo- CSM to test the validity over a long integration period Designed suite of forcings to probe model sensitivity for the 20 th 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

20 th Century Climate Climate models with only “natural” forcings (volcanic and solar) do not reproduce observed late 20 th century warming When increases in anthropogenic greenhouse gases and sulfate aerosols are included, models reproduce observed late 20 th 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 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 Inference for Climate Change Circa the Third Assessment Report Regional Statistical Analysis 9 AOGCM Projections A2 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: j= 0: observed data j=1, M: the M models X: current climate Y: future projection   and  :  true values The variance in the errors is determined based on principles of model bias and model convergence e  jj jj Y X    

Posterior Distributions for Current and Future Winter Temperatures (DJF) for Central Asia °C

Inference for the Mean Temperature Change °C

Multivariate Model - Pooling Uncertainties Regions °C

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 20 th 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 20 th century

Large-scale changes in atmospheric circulation affect regional pattern of changes in future frost days H L 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 cold 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

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. American Water Works Research Foundation Collaboration with NCAR

Global Regional WNA ΔTemp, DJF ΔTemp, JJA %PCP, DJF %PCP, JJA Statistical Downscale(Yates et al. 2003) C mm JJA DJF hist CC scenar hist JJA C mm DJF San Joaquin Sacramento Water Resources Assessment

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 Web site development Find and develop points of integration across projects Promote integration with other initiatives Monthly Meetings of Advisory Board – to discuss topics such as: