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Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1 , Joshua S. Fu 1 , John B. Drake 1 , Yang Liu 2 , Jean-Francois Lamarque 3 , Kan Huang 1 , Xinyi Dong 1 and David Wong 4 1 Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN 2 Rollins School of Public Health, Emory University, Atlanta, Georgia 3 Atmospheric Chemistry and Climate and Global Dynamics Divisions, National Center for Atmospheric Research, Boulder, CO 4 Atmospheric Modeling and Analysis Division, NERL, USEPA, Research Triangle Park, NC 11 th Annual CMAS Conference 15-17 th October, 2012
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Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

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Page 1: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Improvement of extreme climate predictions from dynamical climate downscaling

Yang Gao1, Joshua S. Fu1, John B. Drake1, Yang Liu2, Jean-Francois Lamarque3, Kan Huang1, Xinyi Dong1 and David Wong4

1Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN2Rollins School of Public Health, Emory University, Atlanta, Georgia3Atmospheric Chemistry and Climate and Global Dynamics Divisions, National Center for Atmospheric Research, Boulder, CO4Atmospheric Modeling and Analysis Division, NERL, USEPA, Research Triangle Park, NC

11th Annual CMAS Conference

15-17th October, 2012

Page 2: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Linkage from Global model to Regional Model

Community Earth System Model

CESM 1.0

Regional Climate/Chem Model

WRF 3.2.1/CMAQ 5.0

D1/D2/D3: 36-12-4 km

Community Land Model

(CLM)

Community Atmosphere Model (CAM)

Community Sea Ice Model

(CSIM)

Ocean component(POP)

http://dss.ucar.edu (dataset number ds510.6)

The points represent NCDC US COOP network station observation points in three regions: Northeast (red color), Midwest (blue color) and Southeast (green color).

2

1850-2100

2001-2004/2057-2059

Page 3: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

• Representative Concentration Pathways (RCP) 4.5 Low-medium emissions increasing scenario

• Representative Concentration Pathways (RCP) 8.5

Fossil fuel intensive scenario

RCP 8.5

RCP 4.5

Global Climate Simulation Scenarios

RCP:20052100

Source: http://stratus.astr.ucl.ac.be/textbook/pdf/Chapter_6.pdf

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Page 4: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Three hourly simulations were conducted for RCP 4.5 and RCP 8.5

The data has been published in the Earth System Grid and is available to use by the climate community

Three Hourly Global Climate Simulations

http://esg2-gw.ccs.ornl.gov

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Page 5: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Source: CCSP, 2008

Heat wave intensity:

The highest three continuous night minimum temperatures in a certain year

Heat wave duration and frequency:

Two maximum daily temperature thresholds are used for the determination:

T1 and T2 can be

a) fixed values 30º and 25 ºC

b) the 97.5th and 81st percentiles

A heat wave period is:

(1) the maximum daily temperature reached T1 for at least 3 days

(2) the mean daily maximum temperature remained above T1 while the daily maximum temperature reached T2 each day

The Definitions of Heat Waves5

Page 6: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Evaluation of daily maximum temperature (T1/T2)

19 states in WRF and 17 states in CESM have bias less than 2 ºC.

In WRF, more than half of the states (13 out of 23) shows bias less than 1 ºC

Gao et al., submitted to ERL

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97.5% 81%WRF-NCDC

CESM-NCDC

Page 7: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Evaluation of Heat Waves in CESM and WRF

25

54

43

68

69

46

62

13

28

97

91

98

Intensity Duration Frequency

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Page 8: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Wide Increase in Heat Wave Intensity, Duration and Frequency

Heat wave intensity(ºC)

Heat wave duration(days/event)

Heat wave frequency(events/year)

Present RCP8.5 RCP8.5-Present

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Page 9: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

State-Level Increase in Heat Wave Intensity, Duration and Frequency

Intensity Duration Frequency

3.05

2.78

2.46

1.92

1.78

1.23

5.79

4.34

3.77

×

11

×

8

×

5

5

6

5

9

Page 10: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

City-Level Increase in Heat Wave Intensity, Duration and Frequency

Among the top 50 cities by population in US, 20 is located in the eastern US

http://en.wikipedia.org/wiki/List_of_United_States_cities_by_populat

ion

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Page 11: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

City-Level Increase in Heat Wave Intensity, Duration and Frequency 11

Page 12: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

A rainy day is defined as a day when the daily precipitation totals at least 1 mm. In the current analysis, extreme precipitation is defined as the 95th percentile of all the rainy days

• Total extreme precipitation (mm/year): Annual total of extreme daily precipitation amounts

• Annual extreme events (days/year): Number of extreme daily precipitation events

• Daily extreme precipitation (mm/day): Annual mean rate of extreme daily precipitation, which is calculated as the total amount of annual extreme precipitation divided by total annual extreme precipitation days.

Extreme precipitation12

Page 13: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Evaluation of precipitation13

Page 14: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Extreme precipitation

Total extreme precipitation

Annual extreme events

Daily extreme precipitation

Present RCP8.5 RCP8.5-Present

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Page 15: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Maximum Daily 8-hr Ozone (MDA8) 15

Page 16: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

• Dynamical climate downscaling technique has been applied to CESM, and the downscaled results show significant improvement over global outputs, primarily due to the incorporation of local detailed topography and land use information

• In future climate, more intense and frequency heat waves and extreme precipitation were projected

• In RCP 4.5, ozone concentrations show significant decrease by the end of 2050s; In RCP 8.5, ozone concentration could increase from combined climate and emission effects

Summary

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Page 17: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

This research was supported in part by the National Science Foundation through TeraGrid resources provided by National Institute for Computational Sciences (NICS) under grant number [TG-ATM110009].

This research also used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

This work was partially sponsored by the Centers for Disease Control and Prevention (CDC) under a research project cooperative agreement (5 U01 EH000405).

Acknowledgement

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Page 18: Improvement of extreme climate predictions from dynamical climate downscaling Yang Gao 1, Joshua S. Fu 1, John B. Drake 1, Yang Liu 2, Jean-Francois Lamarque.

Thanks for your attention!

Questions?

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