A Northwest Consortium for Regional Climate Modelling Cliff Mass Department of Atmospheric Sciences University of Washington

Society needs reliable information on the regional implications of climate change Infrastructure decisions are being made now for assets that will last well into the current century when the impact of GW will be substantial Adaptation planning Motivation for mitigation efforts

Bottom Line of the Talk There is only one viable method for producing the required regional climate predictions: dynamical downscaling of global climate models But to do this right, there is much that is required.

Requirements The use of large numbers of global model simulations (GCMs) to explore initial condition, model, and physics uncertainty. Filtering to remove GCMs unable to simulate realistically the current climate Use of sufficient resolution in the regional climate models (minimum 12-15 km grid spacing) to get realistic NW circulations Use of an appropriate nested domain structure of the regional simulations (MUST include the Rockies with sufficient resolution) Must have sophisticated post-processing to reduce biases and get reliable probabilities.

Two Sobering Facts No one in the NW has done such downscaling right yet Too few GCM’s applied to dynamical downscaling Primitive post-processing Statistical downscaling Getting the wrong answers from inadequate efforts can be either useless or costly for society. No answer is better than the wrong answer.

Why haven’t we done it correctly? Lack of computer resources and personnel Too few GCM runs available, so uncertainty has not been explored. Lack of filtering of unrealistic GCMs Deficiencies of regional climate models (e.g., snow in NOAH Land Surface Model) Lack of familiarity of some climate groups with mesoscale meteorology of the region

And we have not even talked about another essential component of regional climate prediction Coupling the regional climate simulations with: Hydrological models Air quality models Coastal ocean models Fire models and others

The Challenge: Can We Do This Right, Together? Combine regional resources and personnel so that together we can do what one group cannot accomplish alone. There is a precedent for such combination of resources: the Northwest Modeling Consortium.

Many of the technical issues are very similar….

Dynamical Downscaling Not Statistical Downscaling Only fully dynamical downscaling can hope to simulate the non-linearities and complexities of the future climate response. Examples: Location and distribution of precipitation changes as stability changes. Onshore flow and coastal marine clouds enhanced by greater onshore pressure gradients Albedo feedbacks as snow melts.

Echam-5 versus WRF

Removing Unrealistic GCMs The quality of the available (CMIP-5) GCMs vary, with some unable to produce a realistic simulation of the contemporary climate over the western U.S. These must be removed from consideration for downscaling work.

Dozens of downscaled GCM runs not 1-3 There is substantial uncertainty in the GCM simulations and a handful of simulations can not get a handle on the inherent uncertainties. Sources: physics uncertainty, initial state uncertainty, emission uncertainty, and many more. GCMs produce a wide variety of local solutions

Initial Condition Uncertainy Dr. Clara Deser of NCAR showed that simply by varying the start times of simulations of the same GCM can produce very different regional results decades hence.

Sufficient resolution and proper domains

Over the Northwest, the simulations must have a grid spacing of a least 12-15 km grid spacing Based on over 10 years of twice-daily simulations at 36-12-4-1.3 km at the University of Washington, with objective verification

150 km versus 12 km

36-km

12-km

The Proper Domain Structure is Crucial GCMs have insufficient resolution to define key regional weather features (e.g., major terrain features), resulting in large simulation errors that make their results unreliable for local decision making. Regional climate simulation must have enough resolution (~50km) on a large enough grid to get the Rockies right—or cold air spills over the Northwest from the cold interior

Cold Waves Under Global Warming PCM Cold Wave Under Global Warming 12 Feb 1990

Seattle 2050

PCM and ECHAM-5 Driving Small Domain MM5: Crazy Cold Waves

Sophisticated post-processing Remove systematic bias based on contemporary periods. Weight ensemble of climate simulations based on their quality. Produce calibrated probabilistic guidance.

Use Bayesian Model Averaging, calibrated on a contemporary period, to produce probabilistic regional climate predictions.

Proven approach for weather forecasting

Integration of ancillary physics/applications (e Integration of ancillary physics/applications (e.g, hydro, coastal ocean, air quality) to create a total environmental modeling system

The Northwest Climate Modeling Consortium: A Proposal Combine resources of regional government entities, state and Federal agencies, foundations, academic institutions, tribes, and other to create the most robust climate predictions possible. Similar to the NW modeling consortium, decisions could be make by contributing stakeholders

Tasks Acquire or secure sufficient computer resources for regional climate downscaling and to run additional GCM simulations. Develop GCM filtering approaches. Acquire CMIP-5 (6) GCM runs and run/acquire additional simulations. Run large numbers of regional climate simulations

Tasks Run applications models (e.g., hydro) Optimally post-process and combine regional climate simulations to provide robust probabilistic guidance

The tasks will be distributed over several groups according to resources/ability The University of Washington is the largest group and is willing to take on management responsibilities.

Products Model grids and custom products will be available to supporting stakeholders and their designees. Grids can also be made available to regional climate impacts group (Oregon State, UW CIG, etc.)

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