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Colorado River Basin streamflow projection under IPCC CMIP5 scenarios: from the global to basin scale using an integrated dynamic modeling approach. Hsin -I Chang 1 , Christopher Castro 1 , 1 Department of Atmospheric Sciences University of Arizona Mar 28 th , 2014.

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


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slide1

Colorado River Basin streamflow projection under IPCC CMIP5 scenarios: fromthe global to basin scale using an integrated dynamic modeling approach

Hsin-I Chang1, Christopher Castro1,

1Department of Atmospheric Sciences

University of Arizona

Mar 28th, 2014

slide2

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

  • Sources of climate projection uncertainty for CRB:
    • GCM and emission scenarios used
    • Spatial scale and topography dependency
    • How land surface hydrology represents precipitation and temperature change
    • Downscaling methodologies
slide3

Multi-model schematic:

Global Climate projections (1 to 2.5° degree resolution)

Regional climate simulations (25km resolution)

Basin-scale simulations (1/8 degree resolution)

Dynamical downscaling

Bias correction

slide4

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
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.

annual mean rmse for precipitation 17 core cmip5 models vs cmap observed estimates for nam region
Annual mean RMSE for precipitation: 17 core CMIP5 models vs CMAP observed estimates for NAM region

Sheffiled et al. 2013

regional climate experimental design
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
slide10

Hypothesis: : Increases in warm season precipitation and temperature extremes will be enhanced by natural variability. Dry Gets Drier andWet 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
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)

slide12
Early warm season precipitation significantly related to global sea surface temperature anomalies (CMIP3)

Climate control period

1950-2000

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

Regression of mode on

global SSTA

Climate change period

2000-2040

slide13

Precipitation Extremes Anomaly(following ENSO signal)

Positive ENSO-PDV phase (El Nino)

Dry SW monsoon

Negative ENSO-PDV phase (La Nina)

Wet SW monsoon

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
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

IPCC CMIP3 vs CMIP5 projections for the Southwest

Projected Southwest drying trend is not as dire in AR5

Mean-Annual Precipitation Change, percent

CMIP3, 1970-1999 to 2070-2099, 50%tile

Mean-Annual Precipitation Change, percent

CMIP5, 1970-1999 to 2070-2099, 50%tile

Mean-Annual Precipitation Change, percent

CMIP5 - CMIP3, 1970-1999 to 2070-2099, 50%tile