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Seasonal Predictions of Very Hot Days in HK using NCEP CFSv2 Francis Tam 1,2 , Wai Kit Lo 2 , Kunhui Ye 1 1 Guy-Carpenter Asia-Pacific Climate Impact Centre, City University of Hong Kong 2 School of Energy and Environment, City University of Hong

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Seasonal Predictions of Very Hot Days in HK using NCEP CFSv2

Francis Tam1,2, Wai Kit Lo2, Kunhui Ye1

1Guy-Carpenter Asia-Pacific Climate Impact Centre, City University of Hong Kong

2 School of Energy and Environment, City University of Hong Kong

  • !"
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Introduction – I

Seasonal forecasts are carried out by major operational centers – “Predicting the statistical summary” (ECMWF, 2013) – Dynamical forecast systems (GCM-based) are commonly used – e.g. rainfall, temperature, tropical cyclones (Chan et al. 1998; Chan and

Shi 1999; Zhang et al. 2012, Sohn et al. 2013, Tung et al. 2013)

Seasonal forecast from Hong Kong Observatory since 2007 – Global-Regional Climate Model (G-RCM) from Experimental

Climate Prediction Centre (ECPC)

Configurations of the G-RCM

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Introduction – II

Other types of extremes: heat waves – unpleasant feelings, financial costs (e.g. energy), environmental

loss (e.g. wildfire), health care issues, and even fatalities

Recent study on heat wave predictability in US (Luo and Zhang 2012)

Observations NCEP CFS run initiated in April 2011

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Introduction – III

Can heat waves over China be predicted a season ahead?

Here we study the seasonal predictability of very hot days in Hong Kong Tmax>35o, 3-5d Tmax>35o, >5d

(from T. Ding et al. 2010)

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Data: HKO Observations

Historical observations from Hong Kong

Observatory (HKO)

– Measured at Hong Kong Observatory Hq – 24 times everyday Air Temperature [1947/01/01 – 2008/06/30] Relative Humidity [1965/01/01 – 2008/06/30]

– Time period

1982 to 2007 (26 years) 06/01 to 09/30 (JJAS)

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Data: CFSR Reanalysis

Climate Forecast System Reanalysis (CFSR) (Saha

et al. 2010) – Global, high-resolution

0.5 degree horizontal resolutions 64 vertical levels (surface to 0.26hPa)

– Coupled atmosphere-ocean-land surface-sea ice system – Includes observed CO2 variations, changes in aerosols, other trace

gases and solar variations

– Variables used:

Temperature @ sigma=0.995: TMP_P0_L104_GLL0 Relative Humidity @ sigma=0.995: RH_P0_L104_GLL0

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Data: Hindcast experiments by CFS

Climate Forecast System version 2 (CFSv2) (Saha et

  • al. 2013)

– Quasi-global, fully coupled atmosphere-ocean-land model R2 NCEP/DOE Global Reanalysis: for atm. & land surfaces initial conditions GODAS: for ocean initial states GFS: for atmospheric model MOM3: for the ocean component – New in version 2 Four level soil model Interactive three layer sea-ice model – Variables used

Temperature @ 2m: TMP_P0_L103_GGA0 Specific Humidity @ 2m: SPFH_P0_L103_GGA0

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School of Energy and Environment CFS Hindcast Configuration

Hindcast Configuration for CFSv2 (T126L64)

Jan 1 0 6 12 18 9 month run 1 season run 45 day run Jan 2 0 6 12 18 Jan 3 0 6 12 18 Jan 4 0 6 12 18 Jan 5 0 6 12 18 Jan 6 0 6 12 18 1st 6th 11th 16th 21st 26th 31st

6 12 18 6 12 18 6 12 18 6 12 18 6 12 18

… 28 in total

6 12 18 6 12 18

7x4=28 Hindcast Runs initiated in May

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School of Energy and Environment Measurement of Heat Stress to Human

Various approaches to apparent temperature (AT)

(Steadman 1979, Steadman 1984, Steadman 1994) – Wet Bulb Globe Temperature (WBGT)

Developed in the late 1950s for the US Marine Corps Recruit Depot

Designed to be a measure of heat stress for human beings

(American College of Sports Medicine 1984, ISO 7243)

where Tw the wet-bulb temp., Tg the globe temp., Td the dry-bulb temp.

Approximation by HKO (梁延剛 et al. 2009)

WBGT = -12.065 + 1.193 T + 0.0688 RH

WBGT = 0.7 Tw + 0.2 Tg + 0.1 Td

[for outdoor]

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JJAS Mean Anomalies Correlation

Low correlation betn seasonal mean WBGT

– CFSR vs CFSv2: R2 is 0.0888 – HKO vs CFSv2: R2 is 0.0233

WBGT

(JJAS mean anomalies, CFSR vs CFSv2)

WBGT

(JJAS mean anomalies, HKO vs CFSv2)

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Climatological Distributions

26-year WBGT distributions

– No. of days are normalized to 122 for easier comparison – Upper and lower 15% and 33.3% thresholds are shown – Compared to HKO, CFSR and CFSv2 are less skewed

WBGT upper 15% upper 33.3% lower 33.3% lower 15% WBGT upper 15% upper 33.3% lower 33.3% lower 15% WBGT upper 15% upper 33.3% lower 33.3% lower 15%

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Determination of very hot days

Define “Very Hot Days” to be days with WBGT > threshold for upper

15% in climatology

Count no. days each year in each of these categories:

– WBGT > top 15% – WBGT< bottom 15% – WBGT in between

more

(37.5%)

fewer

(4.25%) (15.9%)

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Correlation of no. of day prediction vs obs

R2 = 0.5818

CFSv2 vs CFSR CFSv2 vs HKO

R2 = 0.1964

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Definition of “ “ “ “Hot Season in JJAS” ” ” ”

A “Hot JJAS Season” is one during which “Very

Hot Days” occur more often than climatology

more

(37.5%)

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Hot JJAS seasons

HKO data gives some obvious spikes (e.g., 83’, 87’, 90’, 93’ 98’) Both CFSR and CFSv2 show an increasing trend

5 10 15 20 25 30 35 40 45 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Percentage of days above threshold for 15%

(above 15% by WBGT)

HKO: observations CFSR: reanalysis CFSv2: forecast

Percentage of Very Hot Days in JJAS (WBGT above upper 15% threshold)

Hot Not Hot Not Hot Hot Hot Hot Hot Not Hot Not Hot

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Performance in Classification of Hot / Not Hot Season of JJAS

Rough comparison

– 13-13 distribution of “Hot” and “Not Hot” seasons from HKO – 14-12 ratio in CFSv2 hindcast

Year-by-year comparison

– Performance in Hit Rate (0.769), False Alarm Ratio (0.286) is acceptable,

with Hanssen-Kuipers Discriminant of 0.462

HKO Hot Not Hot CFSv2 Hot 10 4 14 Not Hot 3 9 12 13 13 26 Above 15% Hit Rate 0.769 False Alarm Rate 0.308 False Alarm Ratio 0.286 Hanssen-Kuipers Discriminant 0.462

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Percentage of Very Hot Days Before vs. After Trend Removal

Percentage of Very Hot Days in JJAS (CFSR) Percentage of Very Hot Days in JJAS (CFSv2)

Obvious increasing trend of hot years – CFSR and CFSv2 tend to suggest more “hot” seasons after 1998 Remove linear trends in JJAS mean temperature

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Categorization after trend removal

Rough comparison (Hot vs. Not Hot)

– 10-16 for HKO and 12-14 for CFSv2 – fewer than before trend removal

Slight performance drops after trend removal

– Hit Rate: 0.769 0.700; – False Alarm Ratio: 0.286 0.417 – Hanssen-Kuipers Discriminant: 0.463 0.388

HKO Hot Not Hot CFSv2 Hot 7 5 12 Not Hot 3 11 14 10 16 26 Above 15% Hit Rate 0.700 False Alarm Rate 0.313 False Alarm Ratio 0.417 Hanssen-Kuipers Discriminant 0.388

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Concluding remarks

CFSv2 shows some skill in forecasting the number of “very hot days”

in HK

– R2=0.1964 for WBGT > top 15% threshold from HKO data – R2=0.5815 based on CFSR data CFSv2 gives promising results in capturing the occurrence of a hot

season

– Hit Rate = 0.796; False Alarm Ratio = 0.286 and Hanssen-Kuipers

Discriminant = 0.462

Hit Rate = 0.7 after removal of temperature trend. Thus the mean

temperature trend does not contribute much to the seasonal forecast skill

Where does the seasonal prediction skill comes from? (ENSO?)

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Regression maps of JJAS mean

Based on CFSR data Corr = 0.6 Work is on going!

(DJF)

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Q & A

Thank you!

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References – I

  • American College of Sports Medicine, Prevention of thermal injuries during distance running -

Position Stand. The Medical Journal of Australia, 141(12-13):876-879, Dec 1984.

  • Chan J. C. L., J. E. Shi and C. M. Lam. "Seasonal forecasting of tropical cyclone activity over the

western North Pacific and the South China Sea", Weather and Forecasting, 13:997-1004, 1998.

  • Chan J. C. L. and Shi J. E. "Prediction of the summer monsoon rainfall over South China",

International Journal of Climatology, 19:1255-1265, 1999.

  • Ding T. and Qian W. H. "Statistical characteristics of heat wave precursors in China and model

prediction", Chinese J. Geophys. (in Chinese), 5(5): 1472-1486, 2012.

  • Dole R., M. Hoerling, J. Perlwitz, J. Eischeid, P. Pegion, T. Zhang, X. W. Quan, T. Xu, and D. Murray.

"Was there a basis for anticipating the 2010 Russian heat wave? ", Geophys. Res. Lett., 38:L06702, 2011.

  • ECMWF, Seasonal Forecast User Guide (System 4)

(http://www.ecmwf.int/products/forecasts/seasonal/documentation/system4/index.html), 2013.

  • Hui, T.W., and Shum, K.Y. “Prediction of Seasonal Rainfall in Hong Kong Using ECPC's Regional

Climate Model”, The Sixth International RSM Workshop, 2005.

  • ISO 7243, Hot Environments - Estimation of the Heat Stress on Working Man.
  • Luo L. F. and Zhang Y. "Did we see the 2011 summer heat wave coming? ", Geophys. Res. Lett.,

39:L09708, 2012.

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References – II

  • Sohn S. J. et al. "Assessment of the long-lead probabilistic prediction for the Asian summer monsoon

precipitation (1983–2011) based on the APCC multimodel system and a statistical model", Journal of Geophysical Research - Atmospheres, doi:10.1029/2011JD016308, 2012.

  • Steadman R. G. "The Assessment of Sultriness. Part I: A Temperature-Humidity Index Based on

Human Physiology and Clothing Science", Journal of Applied Meteorology, 18(7):861-873, July 1979.

  • Steadman R. G. "A Universal Scale of Apparent Temperature", Journal of Applied Meteorology

23(12):1674-1687, 1984.

  • Steadman R. G. "Norms of apparent temperature, Australia", Australian Meteorology Magazine, 43:1-

16, 1994.

  • Tung Y. L., C. Y. Tam, S. J. Sohn and J. L. Chu. "Improving the seasonal forecast for summertime

South China rainfall using statistical downscaling", Journal of Geophysical Research - Atmospheres, 2013.

  • von Storch H., E. Zorita, and E. Cubasch. “Downscaling of global climate estimates to regional

scales: An application to the Iberian rainfall in wintertime”, J. Clim., 6:1161–1171, 1993.

  • Zhang R. F., Y. Q. Wang, M. Ying. "Seasonal Forecasts of Tropical Cyclone Activity over the Western

North Pacific: A Review", Tropical Cyclone Research & Review, 1(3): 307-324, 2012.

  • 梁延剛、陳玉、林鄺泗蓮, "氣溫與熱力指數在酷熱天氣警告業務運作上的應用比較", 第二十三屆粵澳

氣象科技研討會, 2009.

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Long-term Temperature Trend

y = 0.0043x + 19.69 y = 0.0263x - 25.007 y = 0.0207x - 13.058 26 26.5 27 27.5 28 28.5 29 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Temperature / C

Temperature in Celcius

HKO CFSR CFSv2 Linear (HKO) Linear (CFSR) Linear (CFSv2)

JJAS Mean Temperature from 1982 - 2007

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JJAS Mean Temperature Before vs. After Trend Removal

y = 0.0043x + 19.69 y = 0.0263x - 25.007 y = 0.0207x - 13.058 26 26.5 27 27.5 28 28.5 29 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Temperature / C HKO CFSR CFSv2 Linear (HKO) Linear (CFSR) Linear (CFSv2)

JJAS Mean Temperature from 1982 - 2007

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Composites of hot days

Composites based on daily CFSR data

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Composites of hot days

Composite pdfs

HKO CFSR CFSv2

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Overlap of Available Data

HKO (tmp, pres, rh)

2008/06 1965/01

CFSR

2011/03 1982/01

CFSv2

2009/05

HKO (tmp, pres)

1947/01 2007/09 2008/06 1965/01 2011/03 1982/01 2009/05 1947/01 2007/09 1982/05 1982/05

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HKO WBGT Setup

[Source: Image from HKO website]

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WBGT Thresholds

WBGT thresholds for the upper and lower 15/33.3% in the 26-year distributions

lower 15% lower 33.3% upper 33.3% upper 15% HKO 26.034°C 27.264°C 28.587°C 29.201°C CFSR 25.276°C 26.491°C 27.539°C 28.131°C CFSv2 26.091°C 27.271°C 28.280°C 28.840°C

Excerpt from Japanese Society of Biometeorology, “ “ “ “Prevention of Heat Stroke in Daily Life Ver. 3” ” ” ” [in Japanese].

Dangerous (above 31° ° ° °C) Severe Warning (28 – 31° ° ° °C)

High danger for elderly even without rigorous activity, avoid going outdoor and stay in cool indoor area Avoid the sunshine when going outdoor, be careful of danger due to rising indoor temperature

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Japanese Interpretation of WBGT

By Japanese Society of Biometeorology (2008)

[From http://www.med.shimane-u.ac.jp/assoc-jpnbiomet/pdf/shishinVer3.pdf]

Dangerous >31°C Severe Warning 28 – 31°C Warning 25 – 28 °C Caution < 25 °C

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  • No. of Very Hot Days in JJAS (HKO)
  • No. of Very Hot Days in JJAS (HKO)
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HKO JJAS Mean Temperature Trend

Original data: 0.0043°C per year Remove top-3: 0.0106°C per year Remove top- & bottom-3: 0.0067°C per year

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26-year Distributions (WBGT, TEMP)