Climate change Climate change and and tropical total lightning - - PowerPoint PPT Presentation

Climate change Climate change and and tropical total lightning tropical total lightning

• R. Albrecht
• R. Albrecht1

1,

, W. Petersen

• W. Petersen2

2, D. Buechler

, D. Buechler3

3,

,

• S. Goodman
• S. Goodman4

4, R. Blakeslee

, R. Blakeslee2

2, H. Christian

, H. Christian3

3

1 1 CICS/ESSIC University of Maryland, College Park, MD

CICS/ESSIC University of Maryland, College Park, MD

2 2 NASA Marshal Space Flight Center , Huntsville, AL

NASA Marshal Space Flight Center , Huntsville, AL

3 3 University of Alabama in Huntsville, Huntsville, AL

University of Alabama in Huntsville, Huntsville, AL

4 4 NOAA/STAR-NESDIS, Camp Springs, MD

NOAA/STAR-NESDIS, Camp Springs, MD

2009 AGU Fall Meeting 2009 AGU Fall Meeting

Motivation

 Evidence of global temperature increase and

precipitation increase in the IPCC models:

 they agree on increasing temperature with increasing CO2

concentrations;

 they agree on increased accumulated precipitation by the

heavy and very heavy events.

Precipitation anomalies by extreme events Temperature warming by scenario

IPCC (2007)

Motivation

 On the average precipitation IPCC (2007) shows

conflicting responses over the 3 main convective tropical regions:

 +60 mm/yr in Southeast Asia/Maritime Continent  Increase in East Africa, but decrease in West Africa  -30 mm/yr in South America

 Moreover, recently the Tropical Measuring

Mission (TRMM) revealed that on a regional annual mean scale less precipitation implies more lightning: a paradox (Price, 2009; Takayabu, 2006; Petersen and Rutledge, 2001).

 11-years of TRMM measurements (1998-2008)

Motivation

 The goal of this study is to investigate the

lightning trends from the 11-years of the Lightning Imaging Sensor (LIS) onboard of TRMM satellite.

Objective

Data and Methodology

 Flashes from LIS orbit dataset at 0.5o resolution:

 Post-boost orbits field of view were corrected by the pre-boost

swath and LIS detection efficiency;

 Cummulated daily flashes and viewtimes using a 49-day

moving window to capture a full diurnal cycle (Boccippio et at., 2000);

 Cummulated flash rate density pentads (5-days).

Data and Methodology

 Flashes from LIS orbit dataset at 0.5o resolution:

 Post-boost orbits field of view were corrected by the pre-boost

swath and LIS detection efficiency;

 Cummulated daily flashes and viewtimes using a 49-day

moving window to capture a full diurnal cycle (Boccippio et at., 2000);

 Cummulated flash rate density pentads (5-days).

 Quantile linear regression calculated for Regional

Boxes:

Data and Methodology

 Quantile linear regression:

 Method to estimate the change (trend) of flash rate density

(FRD) quantiles as a function of the year;

 A quantile is a point taken from the inverse cumulative

distribution function of the FRD so that, for examples, the 0.7 quantile is the value such that 70% of the pentad FRD have FRD below this value (70th percentile);

 A linear quantile regression model (Koenker and Bassett,

1978) assumes that the regressand y (in our case FRD pentads) is linearly dependent on K explanatory variables, and the τth quantile of the error term ετ(t) = 0:

y t=0t∑k=1

K

k x tkt Q∣x t1,..., x tk=0 Qy t∣x t1,...,x tk=0∑k =1

K

k xtk

Data and Methodology

 The coefficients βk(τ) are estimated for 19 different quantiles

(τ=0.05,0.10,...,0.95) using each time the entire dataset of a regional box.

 All statistics were performed using the software R and its

quantile regression package quantreg (Koenker, 2009).

 South East United States + Golf of Mexico (Land):

Results

Results

 South East United States + Golf of Mexico (Land):

Results

 South East United States + Golf of Mexico (Land):

Results

 South East United States + Golf of Mexico (Ocean):

Results

 Southern South America (Land):

Results

 Central Africa (Land):

Results

 Maritime Continent (No Mask):

Results

 Summary for Q(0.95):

Summary for Q(0.95):

 Circles = Land trend (or No Mask)  Triangles = Ocean trend  Blue = negative trend on the 95th percentile 

Red Red = positive trend positive trend on the 95th percentile (only trends with 95% confidence is shown)

Results

 When looking for trends by season...

Discussion and Conclusions

 11-years is a very small period be considered a

climate trend.

 But, if it is a sign of global change, how can we

explain a decrease in the high flash rate densities?

Discussion and Conclusions

 Decrease in the convective mass flux (Betts 1998;

Held et al., 2006; Vecchi and Soden 2007):

 Following Clausius-Clapeyron (C-C):

where α(T) ≈ 0.07 K-1, that is: es ↑7% for each 1-K increase in T

 The global-mean precipitation P is given by the convective

mass flux Mc and the typical boundary layer mixing ratio q: and following C-C:

d lnes dT = L RT

2≡T 

P=Mcq M c M c =P P −0.07T

Discussion and Conclusions

 All IPCC AR4 models show a 10-20% decrease in mass flux

by the year 2100:

 Decrease in convective mass flux could be interpretated as

decrease in updrafts, decrasing clould electrification.

Vecchi and Soden (2007)

Discussion and Conclusions

 Moreover, in warmer climates, IPCC AR4 models

projected elevated cloud base heights (CBHs):

 Yoshida et al (2009) presented that theoretically there

should be a fifth power relationship for lightning activity and cold-cloud depth (D): where:

NSFC = number of lighting flashes per sencond per convective cloud dQ/dt = charging rate B'' = constant and scale independent D = distance between freezing level and cloud top (cold-cloud depth).

 ↑CBHs → ↓D → ↓NSFC and lightning.

NSFC∝ dQ dt =B ' ' D

5

Discussion and Conclusions

 OR some instrument limitations:

 If convective strength is increasing and therefore there is

also an increase in the cold cloud depth:

 Thick cloud depths (>13km) decreases the LIS

detection efficiency for flashes.

 More investigation on the causes of negative flash

rate density trends around the tropics is needed, as well as its the interannual variabilty (seasons).