HIGH-RESOLUTION CLIMATE PROJECTIONS everyone wants them, how do we get them? KATHARINE HAYHOE

HIGH-RESOLUTION CLIMATE PROJECTIONS everyone wants them, how do we get them? KATHARINE HAYHOE TEXAS TECH UNIVERSITY ATMOS RESEARCH

We produce heat-trapping gases THE CLIMATE PROBLEM INCREASING GHG EMISSIONS & CONCENTRATIONS

THE CLIMATE PROBLEM We produce heat-trapping gases What can we expect in the future? RAPIDLY INCREASING GLOBAL TEMPERATURE Global mean temperature this decade likely higher than any time in the last millennium. Future temperature projected to increase at an unprecedented rate. Rapid increase implies significant consequences for human society and the natural environment. Quantifying impacts is essential to robust adaptation strategies & sound mitigation policies.

FROM GLOBAL TO LOCAL We produce heat-trapping gases What can we expect in the future? HOW DO WE GET THERE?

PROBLEM 1: SPATIAL SCALE Problem: We produce GCM heat-trapping output IS TOO gases COARSE What can we expect in the future? OUR CLIMATE CAN BE VERY LOCAL Toronto, ON Buffalo, NY Kissing Bridge, NY

PROBLEM 1: SPATIAL SCALE Problem: We produce GCM heat-trapping output IS TOO gases COARSE What can we expect in the future? OUR CLIMATE CAN BE VERY LOCAL Toronto, ON 150 + 92 53 Buffalo, NY Kissing Bridge, NY YYZ BUF KB

PROBLEM 1: SPATIAL SCALE Problem: We produce GCM heat-trapping output IS TOO gases COARSE What can we expect in the future? GLOBAL MODEL RESOLUTION IS TOO COARSE Toronto, ON 150 + 92 53 Buffalo, NY Kissing Bridge, NY YYZ BUF KB

PROBLEM ARE NOT 2: COORDINATED VARIABLES Problem: We produce GCM heat-trapping output IS TOO gases COARSE What can we expect in the future? GLOBAL MODEL OUTPUT IS NOT ALWAYS RELEVANT A Global climate models Temperature Radiation Fluxes Precipitation Atmospheric circulation and dynamics B Impact analyses Degree-Days Photosynthetically- Active Radiation Streamflow Extreme events

PROBLEM ARE NOT 2: COORDINATED VARIABLES How Problem: What We produce can GCM we we heat-trapping quantify output expect IS future TOO the gases impacts? COARSE future? GLOBAL MODEL OUTPUT IS NOT ALWAYS RELEVANT A Global climate models Downscaling Translation Temperature Regional modeling Radiation Fluxes Statistical downscaling Precipitation Impact modeling Atmospheric circulation and dynamics B Impact analyses Degree-Days Photosynthetically- Active Radiation Streamflow Extreme events

WHAT ARE IS NOT DOWNSCALING? COORDINATED What Problem: We produce is statistical GCM heat-trapping output downscaling? IS TOO gases COARSE What can we expect in the future? STATISTICAL MODELING OBS GLOBAL MODEL Simulation of sub-gridscale variables from coarser-resolution fields Based on assumption that variables at finer resolution than the spatial or temporal scale of the input are reproducible function of large-scale features resolvable by GCMs Source: Hayhoe et al. 2009. MITI

WHAT ARE IS NOT DOWNSCALING? COORDINATED What Problem: is statistical GCM output downscaling? IS TOO COARSE Insert What We produce can a slide we heat-trapping of expect a weather the gases system future? UNDERLYING ASSUMPTIONS

What Simple: VARIABLES STATISTICAL is delta statistical ARE downscaling DOWNSCALING NOT downscaling? COORDINATED METHODS Insert What We produce can a slide we heat-trapping of expect a weather the gases system future? Problem: GCM output IS TOO COARSE TEMPERATURE SIMPLE: MONTHLY/SEASONAL DELTA Most commonly used method in ecological applications Can be applied to any pair of simulated and observed variables Good choice for impacts affected primarily by seasonal or annual mean temperatures Used in many studies for the continental U.S. and Alaska under various names and guises.

What Intermediate: Simple: VARIABLES STATISTICAL is delta statistical ARE Monthly downscaling DOWNSCALING NOT downscaling? COORDINATED Quantile Mapping METHODS Insert What We produce can a slide we heat-trapping of expect a weather the gases system future? Problem: GCM output IS TOO COARSE INTERMEDIATE: MONTHLY QUANTILE MAPPING Most commonly used method in hydrological applications in US Good choice for impacts that depend on both means and variability over timescales of weeks to months Used in the BCSD approach (developed by Wood, Maurer et al.)

STATISTICAL ARE DOWNSCALING NOT COORDINATED METHODS Advanced: Simple: delta Statistical downscaling Asynchronous Regression What Problem: is statistical GCM output downscaling? IS TOO COARSE Insert What We produce can a slide we heat-trapping of expect a weather the gases system future? ADVANCED: QUANTILE REGRESSION Fits piecewise linear regression by month to appropriate predictor variable Simulates observed cumulative distribution Modified from Dettingeret al (2004) based on O Brien (2001); represents special case of more generalized quantile regression approach of Koenker& Bassett (1978)

STATISTICAL ARE DOWNSCALING NOT COORDINATED METHODS Advanced: Simple: delta Statistical downscaling Asynchronous Regression What Problem: is statistical GCM output downscaling? IS TOO COARSE Insert What We produce can a slide we heat-trapping of expect a weather the gases system future? ADVANCED: QUANTILE REGRESSION First operational implementationin upcoming USGS national database Good choice for impacts that depend on daily means & variability over timescales of days to weeks

STATISTICAL ARE DOWNSCALING NOT COORDINATED METHODS Expert: Advanced: Simple: Mixture/NHMM delta Statistical downscaling Asynchronous pattern simulation Regression What Problem: is statistical GCM output downscaling? IS TOO COARSE Insert What We produce can a slide we heat-trapping of expect a weather the gases system future? EXPERT: MIXTURE MODEL/NHMM Experimental use only; operational version under development Good choice for precipimpacts, particularly over complex terrain 1.Define circulation patterns by applying a mixture model to multiple reanalysis fields (Td, Z) 2.Define precipitation patterns by applying a hierarchical ascending clustering method to station-level precip records 3.Model the transition probabilitiesbetween patterns using a nonhomogeneous Markov model Vrac, Hayhoe et al. (2007, 2008) Similar efforts by Schoof, Pryor et al. (2010)

DYNAMICAL ARE DOWNSCALING NOT COORDINATED METHODS Advanced: Simple: delta Statistical downscaling Asynchronous Regression What is statistical downscaling? Problem: Insert What We produce is can a regional slide GCM we heat-trapping of output expect modeling? a weather IS TOO the gases system COARSE future? REGIONAL CLIMATE MODELING Upper Diffusion Eddy Cloud Aerosol Const DIF L2.5 TKE L2 3D DEF Radiation LW + SW RadExt MISC CAM AER GSFC CCC GFDL CSIRO F-L Orbit Gases Aerosols Surface SfcExt VEG SST OCN UCM Urban BEP Land Ocean SLAB RUC PX NOAH CSSP CROP SOM UOM PBL Cumulus YSU ACM GFS MYJ MYNN QNSE Boulac CAM UW ORO Microphysics BMJ NKF SAS GD G3 Kessler[2] Thompson[7] Lin[6] Hong[3] Hong[5] Hong[6] UW ZML CSU GFDL MIT ECP Zhao-Carr[2] Tao[5] Morrison[10] Hong[7] Hong[8]

DYNAMICAL ARE DOWNSCALING NOT COORDINATED METHODS Advanced: Simple: delta Statistical downscaling Asynchronous Regression What is statistical downscaling? Rainfall (summer 1993) Problem: Insert What We produce is can a regional slide GCM we heat-trapping of output expect modeling? a weather IS TOO the gases system COARSE future? NOT ALL REGIONAL MODELS ARE EQUAL Precipitation OBS WRF CWRF JULY 1993 HEAVY RAINFALL EVENT X. Liang (2010) UIUC

DYNAMICAL ARE NOT OR COORDINATED STATISTICAL Advanced: Simple: delta Statistical downscaling Asynchronous Regression What is statistical downscaling? Rainfall (summer 1993) Problem: How Insert What We produce is do can a regional slide we GCM we pick heat-trapping of output expect modeling? a weather method? IS TOO the gases system COARSE future? HOW DO WE PICK A DOWNSCALING METHOD?!

COMPARING DOWNSCALING METHODS What Advanced: Simple: VARIABLES Problem: For How Insert What We Rainfall some produce is do delta can a statistical regional (summer variables, slide we GCM Statistical ARE we pick heat-trapping downscaling of NOT output 1993) expect modeling? a future downscaling? Asynchronous weather COORDINATED method? IS projections TOO the gases system COARSE future? Regression are relatively FOR insensitive SOME INDICES, to downscaling RESULTS ARE method SIMILAR Accumulated degree-days more complex downscaling approaches Source: K. Hayhoe. In preparation.

COMPARING DOWNSCALING METHODS What For Advanced: Simple: VARIABLES Problem: For How Insert What We Rainfall others some produce is do delta can a statistical regional (summer (like variables, slide we GCM Statistical ARE we extremes), pick heat-trapping downscaling of NOT output 1993) expect modeling? a future downscaling? Asynchronous weather COORDINATED method? future IS projections TOO the projections gases system COARSE future? Regression are very sensitive relatively to insensitive downscaling to method downscaling method FOR OTHER INDICES, FUTURE PROJECTIONS ARE VERY SENSITIVE TO DOWNSCALING METHOD Days per year > 95 o F more complex downscaling approaches Source: K. Hayhoe. In preparation.

COMPARING ARE DOWNSCALING NOT COORDINATED METHODS For Advanced: Simple: For others some delta (like variables, Statistical extremes), downscaling future Asynchronous future projections projections Regression are very What Future Problem: How Insert What We Rainfall produce is do projections can a statistical regional (summer slide we GCM we pick heat-trapping of output 1993) expect modeling? a downscaling? can weather method? IS be TOO the very gases system COARSE future? sensitive sensitive to downscaling method to relatively downscaling insensitive method to downscaling method AT HIGHER TAIL OF DISTRIBUTION, FUTURE PROJECTIONS VERY SENSITIVE TO DOWNSCALING METHOD Chicago Atlanta daily maximum temperature 2080-2099

COMPARING ARE DOWNSCALING NOT COORDINATED METHODS For Advanced: Simple: For others some delta (like variables, Statistical extremes), downscaling future Asynchronous future projections projections Regression are very What Future Problem: How Insert What We Rainfall produce is do projections can a statistical regional (summer the slide we GCM we methods pick heat-trapping of output 1993) expect modeling? a downscaling? can weather method? IS stack be TOO the very gases system up? COARSE future? sensitive sensitive to downscaling method to relatively downscaling HOW DO insensitive THE DIFFERENT method to downscaling APPROACHES method STACK UP? Simple methods are (surprisingly) reliable for simulating climatological means More complex methods are needed to simulate changes in thresholds and extremes No method not even a regional climate model is guaranteed to successfully correct for GCM bias in multi-day events GCM evaluation and selection is key to accurate simulation of multi-day events

COMPARING ARE DOWNSCALING NOT COORDINATED METHODS For Advanced: Simple: For others some delta (like variables, Statistical extremes), downscaling future Asynchronous future projections projections Regression are very Future What projections can we expect can be in the very future? sensitive sensitive to downscaling method to relatively downscaling insensitive IMPORTANT method to CONSIDERATIONS downscaling method What Choosing Problem: How Insert We Rainfall produce is do a statistical regional (summer slide we GCM assessment pick heat-trapping of output 1993) modeling? a downscaling? weather method? IS tools TOO gases system COARSE Regional models Dynamical regional climate models need to be carefully evaluated, not used as off the shelf black boxes Statistical downscaling models need to be similarly evaluated It is important to compare output of downscaling with observations to make sure any biases are wellknown

NEW APPROACHES ARE NOT COORDINATED TO CLIMATE DATA For Advanced: Simple: For others some delta (like variables, Statistical extremes), downscaling future Asynchronous future projections projections Regression are very What Future Problem: How Insert What We Rainfall produce is do happened projections can a statistical regional (summer the slide we GCM we methods pick heat-trapping of output 1993) expect modeling? a behind downscaling? can weather method? IS stack be TOO the very gases system up? scenes? COARSE future? sensitive sensitive to downscaling method to relatively downscaling TRANSLATING insensitive method to CLIMATE downscaling PROJECTIONS method CLIMATE PROJECTIONS 0.5 degree grids AVERAGES by basin or other region

Simple: What RECOMMENDATIONS CLIMATE is delta statistical SIMULATIONS downscaling downscaling? AND CONCLUSIONS FOR ALASKA Advanced: Statistical Asynchronous Regression Rainfall (summer 1993) What Insert How We produce is do happened maps regional the we methods pick of heat-trapping Alaska modeling? behind method? stack the gases up? scenes? Temperature of hottest day (degrees C) 1969-2000 Cumulative precipitation on wettest day (mm/day) 1960-2000

Simple: What RECOMMENDATIONS RECOMMENDATIONS is delta statistical downscaling downscaling? AND & CONCLUSIONS CONCLUSIONS Advanced: Statistical Asynchronous Regression Rainfall (summer 1993) What How We produce is do happened regional the we methods pick heat-trapping modeling? behind a method? stack the gases up? scenes? Nearly any downscaling method is better than using climate model output directly The ideal downscaling approach for any given analysis depends on the research question being asked Understanding limitations & biases in methods can help select appropriate method or interpret results

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