Hsin -I Chang 1 , Christopher Castro 1 , 1 Department of Atmospheric Sciences

Colorado River Basin streamflow projection under IPCC CMIP5 scenarios: from

the global to basin scale using an integrated dynamic modeling approach

Hsin-I Chang1, Christopher Castro1, 1Department of Atmospheric Sciences

University of ArizonaMar 28th, 2014

Understanding uncertainties in future Colorado River streamflow (BAMS article, Jan 2014)

• Sources of climate projection uncertainty for CRB: 1. GCM and emission

scenarios used2. Spatial scale and

topography dependency3. How land surface

hydrology represents precipitation and temperature change

4. Downscaling methodologies

Multi-model schematic:

Regional climate simulations (25km resolution)

Basin-scale simulations (1/8 degree resolution)

Dynamical downscaling

Bias correction

Global Climate projections (1 to 2.5° degree resolution)

%

Regional Climate Research using IPCC CMIP3 and CMIP5 climate projections

• CMIP3: NARCCAP (North American Regional Climate Change Assessment Program)– Time slice

simulations: [1971-2010], [2041-2070]

– 50km in resolution

• CMIP5: CORDEX NA (Coordinated Regional climate Downscaling Experiment, North America)• 25km resolution:

Continuous simulations [100+ years)

• 12km resolution: time-slice simulations

Climate-Hydrology Projection Research (DOI)

• Objective: Characterize how the changing climate affects seasonal precipitation and streamflow projections in the Colorado River basins– Use the newest climate projections [IPCC CMIP5] that

has good 20th century climatology

• Research Question: How climate trends (mean and extremes) may change in the future, to anticipate worst-case scenarios in long-term water resource planning.

Complete CMIP5 model listModel Center Atmospheric

Horizontal Resolution(lon. x lat.)

Number of model levels

Reference

ACCESS1-0 Commonwealth Scientific and Industrial Research Organization/Bureau of Meteorology, Australia

1.875 x 1.25 38 Bi et al. (2012)

BCC-CSM1.1* Beijing Climate Center, China Meteorological Administration, China

2.8 x 2.8 26 Xin et al. (2012)

CanCM4 Canadian Centre for Climate Modelling and Analysis, Canada

2.8 x 2.8 35 Chylek et al. (2011)

CanESM2* Canadian Center for Climate Modeling and Analysis, Canada

2.8 x 2.8 35 Arora et al. (2011)

CCSM4* National Center for Atmospheric Research, USA 1.25 x 0.94 26 Gent et al. (2011)

CESM1-CAM5-1-FV2

Community Earth System Model Contributors (NSF-DOE-NCAR)

1.4 x 1.4 26 Gent et al. (2011)

CNRM-CM5.1* National Centre for Meteorological Research, France 1.4 x 1.4 31 Voldoire et al. (2011)

CSIRO-MK3.6* Commonwealth Scientific and Industrial Research Organization/Queensland Climate Change Centre of Excellence, AUS

1.8 x 1.8 18 Rotstayn et al. (2010)

EC-EARTH EC-EARTH consortium 1.125 x 1.12 62 Hazeleger et al. (2010)

FGOALS-S2.0 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences

2.8 x 1.6 26 Bao et al. (2012)

GFDL-CM3* NOAA Geophysical Fluid Dynamics Laboratory, USA 2.5 x 2.0 48 Donner et al. (2011)

GFDL-ESM2G/M* NOAA Geophysical Fluid Dynamics Laboratory, USA 2.5 x 2.0 48 Donner et al. (2011)

GISS-E2-H/R* NASA Goddard Institute for Space Studies, USA 2.5 x 2.0 40 Kim et al. (2012)

HadCM3* Met Office Hadley Centre, UK 3.75 x 2.5 19 Collins et al. (2001)

HADGEM2-CC (Chemistry coupled)

Met Office Hadley Centre, UK 1.875 x 1.25 60 Jones et al. (2011)

HadGEM2-ES* Met Office Hadley Centre, UK 1.875 x 1.25 60 Jones et al. (2011)

INMCM4* Institute for Numerical Mathematics, Russia 2 x 1.5 21 Volodin et al. (2010)

IPSL-CM5A-LR* Institut Pierre Simon Laplace, France 3.75 x 1.8 39 Dufresne et al. (2012)

IPSL-CM5A-MR Institut Pierre Simon Laplace, France 2.5 x 1.25 39 Dufresne et al. (2012)

MIROC4h Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan

0.56 x 0.56 56 Sakamoto et al. (2012)

MIROC5* Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan

1.4 x 1.4 40 Watanabe et al. (2010)

MIROC-ESM* Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies

2.8 x 2.8 80 Watanabe et al. (2010)

MIROC-ESM-CHEM

Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies

2.8 x 2.8 80 Watanabe et al. (2010)

MPI-ESM-LR* Max Planck Institute for Meteorology, Germany 1.9 x 1.9 47 Zanchettin et al. (2012)

MRI-CGCM3* Meteorological Research Institute, Japan 1.1 x 1.1 48 Yukimoto et al. (2011)

NorESM1-M* Norwegian Climate Center, Norway 2.5 x 1.9 26 Zhang et al. (2012)

Model RMSE (mm day-1)BCC-CSM1-1 1.92CCSM4 1.53CNRM-CM5 1.29CSIRO-Mk3 1.09CanESM2 0.44GFDL-CM3 1.54GFDL-ESM2M 1.72GISS-E2-R 1.46HadCM3 0.63HadGEM2-ES 0.75INMCM4 1.11IPSL-CM5A-LR 0.99MIROC-ESM 1.32MIROC5 1.58MPI-ESM-LR 1.09MRI-CGCM3 2.08NorESM1-M 1.96

Annual mean RMSE for precipitation: 17 core CMIP5 models vs CMAP observed estimates for NAM region

Sheffiled et al. 2013

Regional Climate Experimental Design

• Weather Research and Forecasting model (WRF)– Forcing from IPCC CMIP5(2) datasets– 25km resolution (CORDEX North America domain) • Two 100+ yr continuous simulation (CMIP5, U.S. and

Mexico)– 10km resolution (Southwest U.S.)• 2x2 10-yr simulations (WRF-CMIP5)• Higher resolution (~ 2km) runs will be considered for

Colorado Headwaters domain

Preliminary Results (CMIP3):Regional Climate and Streamflow analysis

Hypothesis: : Increases in warm season precipitation and temperature extremes will be

enhanced by natural variability. Dry Gets Drier and Wet Gets Wetter

Trend in Global Monsoon Precipitation:• Wang et al. 2012: “….. enhanced global summer monsoon not only amplifies the annual cycle of tropical climate but also promotes directly a ‘‘wet – gets – wetter’’ trend pattern and indirectly a ‘‘dry – gets – drier’’ trend pattern through coupling with deserts and trade winds.”• Hsu et al. 2011: “results suggest that in the past 30 years with an increase in the global mean surface temperature, the global monsoon total precipitation is strengthened.

Interannual variability: Teleconnections at monsoon onset (late June, early July)

The onset and variability of North American Monsoon System (NAMS) is partly controlled by warm season atmospheric teleconnections

Teleconnections driven El Niño Southern Oscillation (ENSO) and Pacific Decadal Variability (PDV)Influence monsoon ridge positioning in early summer.

Other drivers of natural variability: Atlantic Mutidecadal Oscillation (AMO) , Indian monsoon, antecedent land surface conditions

Castro et al. (2001)

Early warm season precipitation significantly related to global sea surface temperature anomalies (CMIP3)

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Average of dominant JJ EOFs with a significant relationship to global SST

Regression of mode on global SSTA

Positive ENSO-PDV phase (El Nino)Dry SW monsoon

Negative ENSO-PDV phase (La Nina)Wet SW monsoon

Precipitation Extremes Anomaly(following ENSO signal)

CPC: (1981-2010)-(1950-1980) CPC: (1981-2010)-(1950-1980)

Anti-phase relationship in precipitation variability between the Southwest U.S. and central U.S. is also found in both precipitation climatology and extreme anomaly trend Observed trends in precipitation anomaly is following the natural variability of ENSO signal. Wet – gets – wetter and dry – gets – drier

Precipitation Extremes Anomaly (Positive ENSO)

WRF-MPI: (2001-2040)-(1950-2000)

Obs: (1981-2010)-(1950-1980)

Projected Southwest drying trend is not as dire in AR5

Mean-Annual Precipitation Change, percentCMIP3, 1970-1999 to 2070-2099, 50%tile

Mean-Annual Precipitation Change, percentCMIP5 - CMIP3, 1970-1999 to 2070-2099, 50%tile

Mean-Annual Precipitation Change, percentCMIP5, 1970-1999 to 2070-2099, 50%tile

IPCC CMIP3 vs CMIP5 projections for the Southwest