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Lecture 12, 10/17/13. Climate Dynamics 11:670:461. Alan Robock Department of Environmental Sciences Rutgers University, New Brunswick, New Jersey USA. robock@envsci.rutgers.edu. http://envsci.rutgers.edu/~ robock.

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slide1

Lecture 12, 10/17/13

Climate Dynamics11:670:461

Alan Robock

Department of Environmental Sciences

Rutgers University, New Brunswick, New Jersey USA

robock@envsci.rutgers.edu

http://envsci.rutgers.edu/~robock

slide2

Predictability: How can we predict the climate decades into the future when we can’t even predict the weather for next week?

Predictability of the first kind: Predict the future based on initial conditions, with boundary conditions constant. This is limited by the chaotic nature of the atmosphere, which is a physical system with built-in instabilities, in vertical convection (e.g., thunderstorms) and horizontal motion (e.g., baroclinic instability - development of low pressure systems, such as hurricanes and Nor’Easters).

slide3

Consider a prediction using the above equation of the future state of the variable X, say the surface air temperature. The subscript n indicates the time, say the day, and a is a constant representing the physics of the climate system. X for any day is a times its value on the previous day minus X squared on the previous day.

With such a simple equation, it should be possible to predict X indefinitely into the future. Right?

slide4

Let’s assume that a is exactly 3.930 and that a prediction with threedecimalplaces is the exact solution.

Then let’s consider three types of errors: imprecise knowledge of the physics of the climate system, imprecise initial conditions, and rounding due to limited computer resources.

This example is from Edward Lorenz.

slide7

Predictability: How can we predict the climate decades into the future when we can’t even predict the weather for next week?

Predictability of the second kind: Predict the future based on boundary conditions, independent of initial conditions. If there are slowly-varying (with respect to the atmospheric predictability limit of 2-3 weeks) boundary conditions (e.g., greenhouse gases, stratospheric aerosols, sea surface temperatures, soil moisture, snow cover) that can be predicted, then the envelope of the weather can be predicted. [The first two examples are external to the climate system, and the last three are internal.]

slide9

ECMWF forecast skill

http://www.ecmwf.int/products/forecasts/d/charts/medium/verification/timeseries/ccafadrian/

slide10

MJO forecast from Dee et al. (2011).

Correlation of> 0.6 has skill.

Dee, D. P., et al., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597. DOI:10.1002/qj.828

slide11

NOAA MJO forecasts

(1) Reanalysis 2 (R2) is used since CFS operational forecast utilizes the R2 as initialization data.

(2) The indices for the latest 4 days are calculated using NCEP GDAS (Global Data Analysis System). 

(3) The MJO definition used here is identical to the Matt Wheeler's (Wheeler and Hendon 2004),  i.e., to represent the MJO, the first two EOFs of combined fields of OLR, u850 and u200 are used.  The followings are some details of the forecast models.

(4) CFS operational:  this is a 2003 version and two member ensemble mean is used.

(5) GFS offline: this runs exactly the same as CFS operational model (e.g. the same R2 initial data) except that air-sea interactionis not allowed.  Four member ensemble mean is used.

(6) GFS operational:  the model keeps being updated.  Model climatology from the GFS offline model is used.  The 11-member ensemble mean is used.

(7) AR: Autoregressive time series model.

(8) PCRLAG: Lagged multiple linear regression.

For details please contact to Kyong-Hwan Seo(khseo@pusan.ac.kr).

http://www.cpc.ncep.noaa.gov/products/people/wd52qz/mjoindex/description_methods_forecasts.html

slide12

NOAA MJO forecast from GEFS model

http://www.cpc.ncep.noaa.gov/products/people/wd52qz/mjoindex/index/diagram_40days_forecast_GEFS_membera.gif

slide13

NOAA MJO forecast from statistical models

http://www.cpc.ncep.noaa.gov/products/people/wd52qz/mjoindex/index/diagram_40days_forecast.gif

slide14

ERA-Interim

Persistence

Dee, D. P., et al., 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597. DOI:10.1002/qj.828

slide15

CONSTRUCTED ANALOG METHOD, Huug van den Dool, http://www.cpc.ncep.noaa.gov/products/people/wd51hd/

slide16

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201310/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201310/

slide17

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201301/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201301/

slide18

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201208/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201208/

slide19

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201203/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201203/

slide20

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201309!/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201309!/

slide21

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201301!/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201301!/

slide22

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201208!/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201208!/

slide23

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201208/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/seasonal_range_forecast/nino_plumes_public_s4!3.4!plumes!201208/

slide24

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201203!/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/eurosip/nino_plumes_euro_public!3.4!201203!/