Prologue Study GCMs (runs) Colorado R Runoff by mid 21st century Christensen et al. 2004 1 (3) -18% Milly 2005 12 (24) -10 to -20% Hoerling and Eischeid 2006 18 (42) -45% Christensen and Lettenmaier 2007 11 (22) -6% (-40 to 18%) Seagar et al. 2007 19 (49) -16% (-8 to -25%) Information from Table 5-1 in Western Water Assessment (WWA) report for Colorado Water Conservation Board “Colorado Climate Change: A Synthesis to Support Water Resource Management and Adaptation.” Oct 2008 (available online at: http://wwa.colorado.edu/publications/reports/WWA_ClimateChangeColoradoReport_2008.pdf)

+ *RISA: Regional Integrated Science and Assessments Brad Udall Dennis Lettenmaier Julie Vano Brad Udall Jonathon Overpeck Holly Hartmann Kiyomi Morino Dan Cayan Hugo Hidalgo Tapash Das Greg McCabe (USGS) Robin Webb (NOAA) Marty Hoerling (NOAA) Levi Brekke (Reclamation) Kevin Werner (NWS RFC) + From abstract: In response, a group of scientists from academic and federal agencies, brought together through a NOAA cross-RISA project, set forth to identify the major sources of disparities and provide actionable science and guidance for water managers and decision makers. This project was developed by the four western RISA centers to address divergent results in Colorado River streamflow, trying to understand and interpret the differences between studies RISA goal: Research that addresses complex climate sensitive issues of concern to decision-makers and policy planners at a regional level. *RISA: Regional Integrated Science and Assessments

Understanding Uncertainties in Future Colorado River Streamflow JAN 2014 Understanding Uncertainties in Future Colorado River Streamflow JA Vano and Collaborators Dennis Lettenmeier, (UW, CIG, CIRC) Brad Udall (WWA) Dan Cayan, Tapash Das, Hugo Hidalgo (CAP) Jonathon Overpeck, Holly Hartmann, Kiyomi Morino (CLIMAS) Robin Webb, Marty Hoerling (NOAA) Greg McCabe (USGS) Levi Brekke (Reclamation) Kevin Werner (NWS RFC) understand how modeling can impact the spread, or uncertainties 14 authors (Regional Integrated Science Assessment)

CLIMATE SURFACE LAND MGMT IMPACT GCM RCM Hydrology model Stream-flow Next set of slides Look more closely at the process Stream-flow MGMT IMPACT Adapted from Vano et al 2014: Figure 1

CLIMATE SURFACE LAND MGMT IMPACT Seager et al. 2007 Seager et al 2013 GCM Seager et al. 2007 Seager et al 2013 Milly et al. 2005 CLIMATE boundary conditions RCM Stat. down P-E, R SURFACE LAND Hydrology model P-E, R flow routing Stream-flow MGMT IMPACT Adapted from Vano et al 2014: Figure 1

CLIMATE SURFACE LAND MGMT IMPACT Seager et al. 2007 Seager et al 2013 GCM Seager et al. 2007 Seager et al 2013 Milly et al. 2005 Christensen et al. 2004 Christensen and Lettenmaier 2007 Cayan et al 2010 USBR 2011 CLIMATE boundary conditions RCM Stat. down P-E, R SURFACE LAND Hydrology model P-E, R flow routing Stream-flow MGMT IMPACT Adapted from Vano et al 2014: Figure 1

CLIMATE SURFACE LAND MGMT IMPACT Seager et al. 2007 Seager et al 2013 GCM Seager et al. 2007 Seager et al 2013 Milly et al. 2005 Christensen et al. 2004 Christensen and Lettenmaier 2007 Cayan et al 2010 USBR 2011 Gao et al 2011 Rasmussen et al 2011 Gao et al 2012 CLIMATE boundary conditions RCM Stat. down P-E, R SURFACE LAND Hydrology model P-E, R flow routing Stream-flow MGMT IMPACT Adapted from Vano et al 2014: Figure 1

Statistical Downscaling GCM, Emission Scenario & Period of Analysis Spatial scale Land Surface models Statistical Downscaling http://www.microassist.com/e-learning-creative-commons-stock-gallery

GCM, Emission Scenario & Period of Analysis Uncertainty #1: GCM, Emission Scenario & Period of Analysis Hydrology model RCM GCM Stream-flow CLIMATE SURFACE LAND Research Objectives: We compare the hydrologic sensitivities of five commonly used land-surface models to temperature and precipitation changes to better understand: To what extent does the land-surface hydrology modulate or exacerbate regional scale sensitivities to global climate change? How do these sensitivities vary spatially across the Colorado River basin? How much of the range of results of these hydrologic sensitivities is attributable to model bias? MGMT IMPACT

-19% -13% Different GCMs, A1B scenario !st source of uncertainty… Adapted from Vano et al 2014: Figure 3

Different GCMs, A1B scenario -19% -13% !st source of uncertainty… -24%

Same GCMs, Different Emissions Scenarios -8% -13% -15%

-8% -17% -15% -10% Same GCMs, Different Emissions Scenarios & Different Periods of Analysis -8% -17% -15% -10%

Lesson #1: 1. Model subset size and composition will impact projections of future streamflow. 2. Emission Scenario and Period of Analysis may matter for some model subsets.

Uncertainty #2: Spatial scale. Hydrology model RCM GCM Stream- flow CLIMATE SURFACE LAND Research Objectives: We compare the hydrologic sensitivities of five commonly used land-surface models to temperature and precipitation changes to better understand: To what extent does the land-surface hydrology modulate or exacerbate regional scale sensitivities to global climate change? How do these sensitivities vary spatially across the Colorado River basin? How much of the range of results of these hydrologic sensitivities is attributable to model bias? MGMT IMPACT

Basin-wide, PRCP amounts are roughly equivalent in summer and winter BUT winter PRCP is much greater in headwaters and more efficiently produces runoff. Images from: (L) http://imagesofrmnp.com/gallery/neversummer/ ® http://www.fs.usda.gov/detail/gmug/home/?cid=stelprdb5399878

15 85 http://travelated.com/hiking-trails-at-rocky-mountain-national-park

0 100 200 300 400 500 600 700 800 900 1000 Runoff (mm/year) another key source of uncertainty… Coarse spatial resolution does not resolve high elevation hydrologic processes that dominate basin runoff production Figure from Vano et al., BAMS, January 2014

0 100 200 300 400 500 600 700 800 900 1000 Runoff (mm/year) 1/8˚ 1/2˚ 1˚ 2˚ another key source of uncertainty… Coarse spatial resolution does not resolve high elevation hydrologic processes that dominate basin runoff production

Annual Average Runoff above 1/8˚ 1/2˚ 1˚ 2˚ another key source of uncertainty… Coarse spatial resolution does not resolve high elevation hydrologic processes that dominate basin runoff production Annual Average Runoff above Lees Ferry (mm/yr) Grid spacing (degrees) Adapted from Vano et al 2014: Figure 4

Annual Average Runoff above Sensitivity: The % change in runoff for an imposed increase in T. another key source of uncertainty… Coarse spatial resolution does not resolve high elevation hydrologic processes that dominate basin runoff production Annual Average Runoff above Lees Ferry (mm/yr) Grid spacing (degrees) Adapted from Vano et al 2014: Figure 4

Lesson #2: Coarser spatial resolutions tend to be more sensitive to change from both warming and precipitation reduction.

Land surface representation. Uncertainty #3: Land surface representation. Hydrology model RCM GCM Stream-flow CLIMATE SURFACE LAND Research Objectives: We compare the hydrologic sensitivities of five commonly used land-surface models to temperature and precipitation changes to better understand: To what extent does the land-surface hydrology modulate or exacerbate regional scale sensitivities to global climate change? How do these sensitivities vary spatially across the Colorado River basin? How much of the range of results of these hydrologic sensitivities is attributable to model bias? MGMT IMPACT

Daily timesteps with some sub-daily processes Image from: http://regclim.coas.oregonstate.edu/wp-content/uploads/bats_small.png Research Objectives: We compare the hydrologic sensitivities of five commonly used land-surface models to temperature and precipitation changes to better understand: To what extent does the land-surface hydrology modulate or exacerbate regional scale sensitivities to global climate change? How do these sensitivities vary spatially across the Colorado River basin? How much of the range of results of these hydrologic sensitivities is attributable to model bias? Grid-based simulations of land-surface processes using principles of energy and water balance Daily timesteps with some sub-daily processes Forcing data: PRCP, T, specific humidity, wind speed, air pressure, and surface incident shortwave and longwave radiation

elasticity* (runoff:prcp) Hydrologic sensitivities For example, catchment elasticity of 3, 5% decrease in precipitation, 15% decline in streamflow, whereas with Noah 2.8 a 5% decline translates to a 11% * elasticity is a ratio of: the percent change in annual model runoff to the percent change in annual precipitation. Adapted from Vano et al 2014: Figure 5

sensitivity* (%∆ per deg C) Hydrologic sensitivities For example, catchment elasticity of 3, 5% decrease in precipitation, 15% decline in streamflow, whereas with Noah 2.8 a 5% decline translates to a 11% * sensitivity is the percent change in runoff for an imposed increase in T. Adapted from Vano et al 2014: Figure 5

Lesson #3: Hydrology models show: 1. substantial differences in sensitivities to T increases; 2. similar responses to precipitation change; 3. differences in P elasticity and T sensitivity are generally smaller in headwater regions.

Statistical downscaling. Uncertainty #4: Statistical downscaling. Hydrology model RCM GCM Stream-flow CLIMATE SURFACE LAND Research Objectives: We compare the hydrologic sensitivities of five commonly used land-surface models to temperature and precipitation changes to better understand: To what extent does the land-surface hydrology modulate or exacerbate regional scale sensitivities to global climate change? How do these sensitivities vary spatially across the Colorado River basin? How much of the range of results of these hydrologic sensitivities is attributable to model bias? MGMT IMPACT

200 km 1 month http://silverspringwanderer.blogspot.com/2013/03/snowquester-one-last-snowshoeing-trip.html 10-20 km sub-daily

+6% (A2 emissions scenario) Global Climate Models Two examples, although there are many new downscaling approaches being developed… see result in differences, but these differences are not as large as differences between GCMs. Comparison of BCSD downscaling from Christensen and Lettenmaier (2007) with a delta method downscaling approach for Lees Ferry in the 2040-2069 future period for the A2 where, on average, the BCSD approach has a decline of 7% whereas with the delta method, declines are 13%.

Lesson #4: The choice of downscaling method can affect the magnitude of the climate signal leading to differences in long-term projected runoff.

Re-cap We identified four major reasons for discrepancies in Colorado River projections: GCMs and emissions scenarios; Spatial scale or the ability of models to simulate the disproportionate contributions to Colorado River discharge of the relatively small, high elevation runoff source areas; Sensitivities of land surface hydrology models to P and T changes; Methods used to statistically downscale GCM scenarios.

Implications for Decision makers From past AR4 studies, we can say with high likelihood that in the Colorado: Temperatures (T) will rise in the Colorado over the coming decades Precipitation (P) less certain, but will likely decline on annual basis From our analysis Warmer T (ignoring P) will reduce runoff production (our estimates -6.5 3.5% per C at Lees Ferry) Change in P results in streamflow response of 2 to 3 times (5% decline in P results in 10-15% decline in streamflow) Coarse spatial resolution of models does not resolve high elevation hydrologic processes that dominate Colorado River basin runoff production

Parting words… The diversity of approaches to projecting future streamflow in the scientific literature requires further analysis to make apples-to-apples comparisons in order to better understand sources of uncertainty.

The Uncertainty Prayer* Grant us… The ability to reduce the uncertainties we can; The willingness to work with the uncertainties we cannot; And the scientific knowledge to know the difference. - Thank you! Research funded by NOAA through its RISA (Regional Integrated Sciences & Assessments) Project and its National Integrated Drought Information System *http://www.wucaonline.org/assets/pdf/actions_whitepaper_120909.pdf

Summary 14 co-authors, from academia and federal agencies, many authors of divergent papers Reached agreement on four key sources of future uncertainties AND important certainties for decision makers in the Colorado River basin Documented for the larger science and management communities how to approach seemingly disparate results, particularly timely with new results being released with the 5th Assessment Report (AR5) In summary, The paper, with it’s 14 co-authors from academia and federal agencies, reached agreement on key sources of uncertainty and important certainties This provides information for the larger science and management communities on how to approach seemingly disparate results. If you remember back to the table of the range of results, with this study we can evaluate the studies, for instance the lowest change value 6% (c&l) is affected by the selection of gcm and downscaling approach, while  the largest value (hoerling) 45% does not capturing the headwaters with there larger spatial resolution .  This will be particularly important with all the new studies that are likely to come out – the results we evaluated here where primarily from AR4. Also, the process helped foster novel research that can help us to better identify how we think about future changes

Future research directions Where research evolving (not ranking of research priorities): New climate change projections Increased spatial resolution of climate models Improved land surface simulations New paleoclimate reconstruction and model evaluation Improved observational records Strengthening connection with management community Lesson 7: As climate science evolves, our understanding of future uncertainties will continue to improve. However, the evidence indicates there is no single magic bullet that will “reduce uncertainties”, nor will uncertainty ever be reduced to zero. Therefore, it is critical that both researchers and water managers redouble efforts and research to incorporate uncertainty and reconcile differences in future projections when possible. This will require continued communication and collaboration between the management and science communities, and will require scientists to more clearly articulate how their studies fit into existing knowledge, and explain how and why their studies do or do not agree with past work. This is a overview of ongoing/future work, not a ranking of research priorities.  As climate science evolves, understandings will improve. However, there will be no single magic bullet. Therefore, a continued effort to communicate and incorporate uncertainty is needed.