A Deficiency In the Grell-Dévényi Cumulus Parameterization

A Deficiency In the GrellDévényi Cumulus Parameterization Department Seminar Nick P. Bassill December 12th, 2012

Outline • Introduction/Motivation • Description of Problem • Global “Case Study” • Conclusions

Introduction • Current NWP models require parameterizations to •

•

•

accurately simulate most meteorological phenomena Parameterization definition: “The representation, in a dynamic model, of physical effects in terms of admittedly oversimplified parameters, rather than realistically requiring such effects to be consequences of the dynamics of the system.”* One class of parameterizations (cumulus parameterizations) are required to represent the cumulative effects of cumulus clouds, particularly when using a model with a horizontal resolution greater than a few kilometers This includes virtually every global model, every climate model, and many mesoscale models

*http://amsglossary.allenpress.com/glossary/search?id=parameterization1

Motivation • There exist many available forms of cumulus • •

• •

parameterization (CP) Frequently, certain parameterizations work well in some weather regimes and not in others Many studies exist which examine parameterization differences, but these generally (1) use short forecast time-scales, (2) are of the case study variety, and (3) study weather over land Broadly speaking, my research largely attempts to do the opposite of these studies, by examining many lengthy (5 day) simulations over the tropical Atlantic Collectively, the CPs studied in this talk have been used in hundreds of AMS publications

Two Literature Examples • Mukhopadhyay et al. (2010) use a nested WRF

•

simulation to simulate precipitation patterns in the Indian Monsoon - They studied the impact of varying CPs on six 4-month simulations of the Indian Monsoon Wehner (2012) examine the occurrence of heavy rainfall among different NARCCAP models - NARCCAP models are regional, downscaled 20+ year simulations used to more accurately simulate the potential effects of climate change

Mukhopadhyay et al. (2010) Left: The contribution to the total seasonal rainfall (%) as a function of rain rate for JJAS

Mukhopadhyay et al. (2010): “This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season”

Wehner (2012)

“Fig. 3 A performance portrait plot showing the models’ percentage error relative to the UW gridded observations in the seasonal mean precipitation, the average seasonal daily maximum precipitation rate and the 20 year return values of seasonal maximum daily precipitation rates in the simulations forced by the NCEP reanalysis. Results are shown for each of the four seasons and are averaged separately over the eastern and western portions of the contiguous US as defined in the text. The seasons are arranged as quadrants in each box as shown in the legend. Units are percent.”

Wehner (2012) Continued • “The most striking features of Fig. 3 are the

large errors exhibited by the WRFG model in the extreme precipitation rate. This discrepancy is puzzling as the mean precipitation rate error is relatively low … The ensemble mean exhibits lower than average model error in the mean precipitation but the large WRFG error adversely effects the mean model’s error in extreme precipitation.”

Description of Parameterizations • Kain-Fritsch (KF): Mass-flux scheme triggered when CAPE • •

is present and upward vertical motion at LCL exists. Betts-Miller-Janjic (BMJ): Adjustment scheme which seeks to relax unstable soundings toward predetermined reference profiles. Grell-Dévényi (G3): 144 member ensemble of mass-flux schemes, consisting of many different triggers (moisture convergence, CAPE removal, upward vertical motion, etc.) and parameters (entrainment rates, precipitation efficiency, etc.). For a given grid point, all members are averaged and output is sent back to the model.

First Dataset • 76 5-day “real-time” ensembles were created during the 2009 Atlantic hurricane season (one every two days) using the WRF model, with 10 members comprised of a variety of differing parameterizations • This presentation will focus on the Grell-Dévényi (Grell and Dévényi, 2002) cumulus parameterization in comparison to two others

WRF-ARW 3.0 Domain 1: 90 km

Domain 2: 30 km

Mean 120 Hour Precipitation 120 Hour Total (mm)

Mean

STD

KF

31.71

4.57

BMJ

28.62

4.57

G3

28.34

4.43

KF

BMJ

• All three CPs display similar

characteristics over 120 hours • However, an analysis of the final 3 hours demonstrates something different • The mean of the standard deviation of 3 hour precipitation over all cases is .11 mm, .11 mm, and .06 mm for the KF, BMJ, and G3 CPs

G3

Further Examination • While the KF and BMJ CPs have similar • •

•

tendencies, the G3 CP seems to perform differently, particularly at shorter intervals Specifically, the G3 produces more (less) light (heavy) precipitation than the other CPs Another way to examine this is to study cumulative distribution functions (CDFs) of 3 hour rainfall totals The following two plots show the evolution of these CPs’ CDFs with time

Model Hour 12

Hour 72

Colors: KF, BMJ, G3

Hour 24

Hour 96

Hour 48

Hour 120

KF Colors: 12 H, 24 H, 48H, 72 H, 96 H, 120 H

BMJ

G3

CDF Conclusions

KF

• Early on, the G3 CP is more •

• •

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likely to produce heavy precipitation than the other CPs After about 24 hours, the opposite is true For all CPs, stability isn’t reached until approximately 72+ hours For the G3 CP, available moisture is a probable cause of these changes Right: Mean difference in 600 hPa relative humidity between forecast hour 72 and forecast hour 6

BMJ

G3

Hypothesis • The ensembling technique the G3 CP employs •

• •

negatively impacts forecasts The extreme drying in regions where rainfall is produced is a sign of an overactive parameterization The G3 CP is overactive in these regions because of the likelihood that some members will produce precipitation Since the G3 CP averages all members, if some produce precipitation and others do not, a likely result will be that there will be more total areas where rain is falling, but that rainfall will be lighter (which is what was observed earlier)

+

(

) / 2 = ??

• What happens when half the members predict a • • •

thunderstorm, and half do not? The result is weaker updrafts, and less intense precipitation Problems arise in moderately unstable, moist, weakly forced environments Examples are air-mass thunderstorm regimes, tropical areas, ITCZ regions, and warm SST regions

EOF Analysis • To demonstrate this phenomenon, an EOF

analysis is performed • EOF analyses are designed to determine dominant modes of variability • Profiles of vertical motion are examined at forecast hour 120 for all simulations at all grid points within the ITCZ (defined as the latitude band 7°-15° N)

KF (72%)

BMJ (59%)

G3 (59%)

This plot shows the leading EOF regressed onto the first principal component for each CP (red), as well as the mean vertical motion (black). The % is the amount of variance the first EOF explains

EOF Analysis Continued • The profile of upward vertical motion associated with

precipitation for the G3 CP is significantly muted relative to the other CPs • This is consistent with observations of lighter rainfall • Given earlier hypotheses, if the ensemble size of the G3 CP were dramatically reduced, it is reasonable to assume that the observed convection differences will be lessened compared with the other CPs • This is tested by recreating the G3 CP simulations in two ways (1) by reducing the G3 CP to an ensemble comprised only of members using a vertical motion closure (2) by reducing the G3 CP to an ensemble comprised only of members using a moisture convergence closure

G3: Omega Threshold (75%)

G3: Moisture Convergence (72%)

G3: Original (59%)

Cumulatively, these EOF analyses suggest that (1) the original G3 CP produces convection characterized by less intense upward vertical motion, and (2) the ensemble nature of the G3 CP is a likely cause of this discrepancy

Second Dataset: Global Case Study • In order to more fully examine the potential G3 • •

CP deficiencies (and any global circulation effects), a series of global WRF simulations are conducted Using the G3 CP and KF CP (for comparison purposes) simulations are created for the period January 1st-February 19th and July 1st-August 24th (2005) Simulation details: – 80 km grid spacing at equator – SSTs updated once daily – Initialized using FNL data (which is also used for comparison purposes) – The first week is omitted for comparison purposes

R

Precipitation Rate -

Upon first glance, rainfall patterns look approximately similar between either CP K F

G 3

G 3 K F

- On closer inspection, the G3 CP produces slightly more precipitation in subtropical high regions - The G3 CP produces less precipitation in regions commonly characterized by high rain rates and over land

• A comparison of R

K F

G 3

G 3 K F

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smoothed outgoing longwave radiation (OLR) suggests that there is more convection over tropical regions with the G3 This is counterintuitive, given the previously analyzed weaker convection produced by the G3 CP

KF

G3

G3-KF

Standard deviation of OLR

KF

G3

G3-KF

Standard deviation of OLR

Preliminary Observations • Both CPs produce roughly similar rainfall • •

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distributions and totals They arrive at these totals in different ways The G3 CP produces widespread weak convection while the KF CP produces less frequent, yet more intense convection over tropical and subtropical regions The remainder of this presentation will examine the potential affect these differences have on the global circulation

KF

G3

G3-KF

Temporally and zonally averaged meridional wind Dotted lines indicate the mean position of the ITCZ as determined by the axis of greatest rainfall

Real

KF

Temporally averaged 250 hPa zonal wind

G3

KF Real

G3 Real

The G3 CP appears to have an equatorial displacement of the mean zonal jet in the winter hemisphere

Zoomed-in Northern Winter Real

KF

G3

One location in which the KF CP and the G3 CP perform very differently is in the northeastern Pacific Ocean

Further Observations • The G3 CP produces a weaker mean Hadley Cell than the

• •

KF CP The primary impact seems to be in the winter hemisphere Simmons et al (1983) demonstrated a significant relationship between tropical forcing and downstream extratropical impacts (below)

KF

G3

G3-KF

342-354 K thickness (hPa)

Conclusions • The ensembling technique of the Grell-Dévényi CP • • •

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causes unrealistic depictions of convection (and attendant precipitation) For larger domains (and longer simulation lengths) this unrealistic convection potentially causes undesirable downstream consequences These problems were likely not found due to the short time scale many parameterization studies use (Grell and Dévényi (2002) used a series of 12 hour simulations when developing their CP) The results shown here corroborate and explain many earlier findings relating to the G3 CP The CP differences also suggest the potential utility in using malfunctioning parameterizations to learn more about atmospheric phenomena

References • Grell, G. A., and D. Dévényi, 2002: A generalized approach to

parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, doi:10.1029/2002GL015311. • Mukhopadhyay, P., S. Taraphdar, B. N. Goswami, K. Krishnakumar, 2010: Indian Summer Monsoon Precipitation Climatology in a High-Resolution Regional Climate Model: Impacts of Convective Parameterization on Systematic Biases. Wea. Forecasting, 25, 369–387. • Simmons, A. J., J. M. Wallace, G. W. Branstator, 1983: Barotropic Wave Propagation and Instability, and Atmospheric Teleconnection Patterns. J. Atmos. Sci., 40, 1363–1392. • Wehner, M., 2012: Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections. Climate Dyn., 22, doi: 10.1007/s00382-012-1393-1

January 7th-February 19th

July 7th-August 24th

Observations Continued • A reduced ability to produce intense convection

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in the G3 CP, particularly near the maritime continent, appears to negatively impact downstream forecast locations (even where the G3 CP is not active) This is analyzed by taking the difference of isentropic trajectories on the 348 K surface (driven by the KF-G3 observed wind), beginning at the initial time through forecast hour 168

Red trajectories initial north of the equator

Blue trajectories initiate south of the equator

Temporally averaged sea level pressure Large sea level pressure differences exist in the same northeastern Pacific Ocean region for northern winter -

- Similarly, storm track differences in the southern winter G3 CP simulation

Note to Michael: I don’t plan on including this slide, but here is the completed histogram. On the y-axis is number of studies and the x-axis is maximum length of parameterization comparison in studies that are not climate studies Given the CDFs I showed earlier, you can see that parameterization stabilization isn’t reached until about 72 hours, making shorter comparisons suspect

Stop Here

Grell Example Continued • A forecast (initialized 00 UTC 19 August) of • •

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Hurricane Bill (2009) is shown for comparison During the time shown, Bill was a powerful hurricane over the central Atlantic Ocean The top panel will show the standard Grell-3 cumulus parameterization The bottom panel will show the Grell-3 using only moisture convergence as a closure

Real-Time Data Generation Overview Dynamical Core is WRF-ARW 3.0

Two Days Between Each Initialization (From GFS 00Z Forecast)

76 Cases From Early June Through October For 2009

Initialization Time

Spread Of 120 Hour Forecasts