Class Outline, Class #10, 16 February ) Review 2) Today s topics - Downscaling, Climate Data Needs - Seasonal Forecasts 3) Discuss Project

Transcription

1 Class Outline, Class #10, 16 February ) Review 2) Today s topics - Downscaling, Climate Data Needs - Seasonal Forecasts 3) Discuss Project

2 Review What is a climate model? What processes are represented in climate models? What is a parameteriza4on? Give some examples. What does it mean to validate a model? How are climate models used?

3 Global Models are too Coarse for Regional InformaNon At 100 km resolunon many processes are not well resolved so we must DOWNSCALE climate informanon for it to be useful! [ACSC Downscaling Workshop 2011]

4 Global Models are too coarse for Regional Information Annual Average Observed Snowfall 54, 92, 150 Lake effect snow 2 scenarios Large spatial gradients in annual average snowfall totals not meaningful at GCM grid scale (2.5 x 2.5 ). [Hayhoe 2010]

5 Statistical vs Dynamical Computationally efficient Can use many scenarios/models Relatively simple process Strengths Can explore why? Gridded data Many climate parameters Daily & hourly data Weaknesses Limited number of variables Need robust observations to train the model data Assume statistical relationship stays same in future Computationally intense Large data to manage Technically challenging Limit to few scenarios/models

6 Dynamical Downscaling for Alaska Regional Model (Weather Research Forecast Model) over extended Alaska Domain (Funded by Alaska Climate Science Center through Dept of Interior) Driven by ERA-Interim reanalysis, GFDL , completed CESM , in progress historical + RCP8.5

7 Climate Gradient is Strong in Alaska [AK Climate Divisions => Bieniek et al. 2012]

8 Alaska is 1/5 size of connnental US & has more coastline Drive Nme Homer- Deadhorse 24 hrs Fly Juneau-Barrow Graphic by Kim Mincer and Vanessa Rathbun (#BLM)

9 Downscale to include more local detail 20km grid 0.75 grid Elevation (m) Downscale WRF ERA Interim WRF has more detailed terrain Reanalysis has broader high elevation areas Can better resolve mesoscale features 1 ~ 110km Bieniek et al. 2016

10 Dynamical Downscaling: Temperatures more RealisNcally Resolved ~80 km Blah Blah 3 July 20-km resolu4on Downscaling captures warm temperatures on a summer day in the valley bohoms.

11 Improved Temperature spatial distribution Interior Alaska, contours are topography of Alaska Range & Interior AK Mar-May Avg Temperature Reanalysis Downscaled Observations [Hill et al.] Contours: WRF topo (m) C Colder at higher elevations as in PRISM-based Hill et al. (2015) monthly observed gridded temperatures Bieniek et al. 2016

12 Improved precipitation spatial distribution Sep-Nov Avg Precipitation Reanalysis Downscaled Observations [Hill et al.] Contours: WRF topo (m) mm Wetter at higher elevations as in Hill et al. (2015) observational data Bieniek et al. 2016

13 Rain-on-snow (icing) events better represented in downscaling than reanalysis ERA Reanalysis ERA Downscaled Obs. [Wilson et al. 2013] April January Avg # ROS days Avg # ROS events Most ROS events in transition months (Oct and April) Direct comparison with observations is difficult Bieniek et al. in prep

14 Downscaling captures expected changes in extreme weather Fewer Frost days More extreme heat Heavier precipitation [Plots: R. Lader]

15 What is the difference between weather and climate? Climate is what we expect and weather is what we get. -Lazarus Long from R. Heinlein s Time Enough for Love Climate tells you what clothes to buy, but weather tells you what clothes to wear. - Anonymous middle school student, [

16 What is the difference between a Weather Forecast & Climate Model? Weather models are used to forecast what is happening in a specific place at a specific time, typically 1-7 days from now. Weather is an initial value problem, depends on the state of the current atmosphere. Climate models do not predict the state of the weather in Fairbanks on March 1, 2070 but they project the average state of the climate for that decade (e.g., warmer or drier than now). Climate is a boundary value problem (boundary conditions vary slowly, ocean, sea ice)

17 El Niño Phenomena Warm water in equatorial Pacific

18 Natural Variability: ENSO Niño 3 Index since Positive ==> Warm event El Niño, Negative ==> Cold Event La Niña

19 El Niño Impacts

20 Current Seasonal Forecast for El Nino North American Multi Model Ensemble

21 Subseasonal to Seasonal S2S Need to expand environmental prediction to fill gap between weather forecasts (< 2 weeks) to climate model (> 1 year) What will the summer precipitation be in Interior Alaska? Wetter or Drier than average? What will the lightning risk be in Interior Alaska in summer 2017? Will it be warmer or cooler than average in summer 2017? Answers to these questions help managers prepare logistics. Ex: Shipping companies, Agriculture need seasonal forecasts

22 Challenges for S2S Forecasts Capture initial state of ocean, land, atmosphere, sea ice Computationally intensive because of long integrations of models at high resolution Make S2S more applicable to users. Less skillful than shorter predictions & are at lower spatial resolution. Hard to communicate the probabilistic information to users!

23 Bold Commihee Vision S2S forecasts will be as widely used a decade from now as weather forecasts are today 1.Engage users in the process of developing S2S forecast products 2.Increase S2S Forecast Skill 3.Improve predictions of extreme and disruptive events and consequences of unanticipated forcing events 4.Include more components of the earth system in S2S forecast models

24 Vision - Strategies - RecommendaNons Visual of the pieces needed to move forward. National Academies of Sciences, Engineering, and Medicine Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. Washington, DC: The National Academies Press. doi: / Fig. S1, NAS 2016

25 Understand Use Barriers CoProduce Products Natural Variability Forecast Extremes ObservaNons Details on 16 RecommendaNons collaboration and coordination is particularly important. Chapter 3 Recommendation A: Develop a body of social science research that leads to more comprehensive and systematic understanding of the use and barriers to use of seasonal and subseasonal Earth system predictions. B: Establish an ongoing and iterative process in which stakeholders, social and behavioral scientists, and physical scientists codesign S2S forecast products, verification metrics, and decisionmaking tools. Chapter 4 C: Identify and characterize sources of S2S predictability, including natural modes of variability (e.g., ENSO, MJO, QBO), slowly varying processes (e.g., sea ice, soil moisture, and ocean eddies), and external forcing (e.g., aerosols), and correctly represent these sources of predictability, including their interactions, in S2S forecast systems. D: Focus predictability studies, process exploration, model development, and forecast skill advancements on high-impact S2S forecasts of opportunity that in particular target disruptive and extreme events. Chapter 5 E: Maintain continuity of critical observations, and expand the temporal and spatial coverage of in situ and remotely sensed observations for Earth system variables that are beneficial for operational S2S prediction and for discovering and modeling new sources of S2S predictability. Research Strategies Basic Research Applied Research/ Operational Benefits Likely in the Short Term 1, 4 1, 4 May Need New Initiative International Collab. Critical 2, 3 3, 2 2, 3, 4

26 Observing PrioriNes Coupled Data AssimilaNon Improve ParameterizaNons Improve sea ice Improve Model EvaluaNons More science on how to use ensemble system Robust OperaNonal Research to OperaNons Develop Capability Details TABLE on S.1 16 Continued RecommendaNons Chapter 5 (cont.) Recommendation F: Determine priorities for observational systems and networks by developing and implementing observing system simulation experiments, observing system experiments, and other sensitivity studies using S2S forecast systems. G: Invest in research that advances the development of strongly coupled data assimilation and quantifies the impact of such advances on operational S2S forecast systems. H: Accelerate research to improve parameterization of unresolved (e.g., subgrid scale) processes, both within S2S system submodels and holistically across models, to better represent coupling in the Earth system. I: Pursue next generation ocean, sea ice, wave, biogeochemistry, and land surface/hydrologic as well as atmospheric model capability in fully coupled Earth system models used in S2S forecast systems. J: Pursue feature-based verification techniques to more readily capture limited predictability at S2S timescales as part of a larger effort to improve S2S forecast verification. K: Explore systematically the impact of various S2S forecast system design elements on S2S forecast skill. This includes examining the value of model diversity, as well as the impact of various selections and combinations of model resolution, number of ensemble perturbations, length of lead, averaging period, length of retrospective forecasts, and options for coupled sub-models. Chapter 6 L: Accelerate efforts to carefully design and create robust operational multi-model ensemble S2S forecast systems. M: Provide mechanisms for research and operations communities to collaborate, and aid in transitioning components and parameterizations from the research community into operational centers, by increasing researcher access to operational or operational mirror systems. N: Develop a national capability to forecast the consequences of unanticipated forcing events. Research Strategies Basic Research Applied Research/ Operational Benefits Likely in the Short Term May Need New Initiative 2, 3, 4 International Collab. Critical 2, 3, 4 2, 3, 4 4, 2, 3 2, 1, 3 2, 3, 4 2, 3 2, 1, 3, 4 3, 1

27 Details on 16 RecommendaNons Cyberinfrastructure Develop Capable Workforce TABLE S.1 Continued Chapter 7 Recommendation O: Develop a national plan and investment strategy for S2S prediction to take better advantage of current hardware and software and to meet the challenges in the evolution of new hardware and software for all stages of the prediction process, including data assimilation, operation of high-resolution coupled Earth system models, and storage and management of results. P: Pursue a collection of actions to address workforce development that removes barriers that exist across the entire workforce pipeline and increases the diversity of scientists and engineers involved in advancing S2S forecasting and the component and coupled systems. Research Strategies Basic Research Applied Research/ Operational Benefits Likely in the Short Term May Need New Initiative Supporting Supporting International Collab. Critical

28 Engage users in the process of developing S2S forecast products Find out what products are needed and match with those that are scientifically feasible! An iterative process of developing products and services! Increase S2S Forecast Skill S2S gets skill from: natural modes of variability, slowly varying processes, external forcing (GHGs) Need: Observations, Coupled data assimilation, improved earth system models, increase resolution, reduce model errors & speed flow from research to operations

29 Improve PredicNon of Extreme/DisrupNve Events Major winter storms, precipitation extremes, monsoon onset and breaks, tropical storms, heat waves Disruptive, consequence from oil spills, volcanic events, 2-12 weeks, probabilistic forecast, chances are x% More components of Earth System in S2S Forecast Models next generation landsurface, sea ice, ocean models needed air quality, vegetation growth, marine ecosystems Cyberinfrastructure & Workforce 1000 fold improvement needed computing, storage, and data transport. Where are the trained people to do this work

30 ParNng Thoughts Downscaling is currently data is provided to many parnes to prepare for the next years. StaNsNcal versus Dynamical downscaling There are uncertainnes in these downscaled data, hard to quannfy Weather forecasnng versus climate forecasnng verses seasonal forecasnng NMME is an example of S2S forecasts in its infancy Need $$ investments and internanonal partnerships to achieve the S2S vision in next decade. 30