Ro Rongqia ian Y Yang, J Jesse Meng Meng , , M Mich chael Ek - PowerPoint PPT Presentation

Ro Rongqia ian Y Yang, J Jesse Meng Meng , , M Mich chael Ek Ek a and He Helin lin W Wei, The EM EMC L Land/Hyd ydro T Team EM EMC/NCEP EP/NWS 5200 A Auth R Road, C Camp S Springs, M MD 2 D 20746, USA USA

 Assess skills of the NCEP CFS v2 in predicting SST , Precipitation , and T2m anomalies.  Examine impact of land surface parameterizations on summer season predictions with the new CFS.

The Atmospheric Forecast Model is increased from T62, L , L28 to T126, L , L64 (~100 km) resolution and equipped with more advanced physics. The Land Surface Model is Introduction of a 3-layer upgraded from the 2-layer global Sea Ic Ice M Model. OSU U to the 4-layer Noah h LSM . LS Fully Coupled Ocean-Land-Atmosphere System, implemented in March, 2011

Precip Pr cip d differenc nce ( (GF GFS-C -CMAP) Glo Global Ga l Gauge Di Distribution n

 The he c critical la l land nd s surface s ski kin t n temperature d depend nds o on r n ratio o of a and nd , , seasona nal G l GVF ( (with c h cons nstant nt L LAI) I), a , and nd o othe hers. .  Bare s soil a l and nd v vegetation a n are t treated t togethe her ( (one ne la layer), In N , In Noah, h, , a , a t tuna nable le p parame meter ( (varies i in d n different nt o operationa nal mo l models ls), i , is used t to c compute , i , is p prescribed f for a all g ll grid c cells lls d depend nding ng o on v n vegetation n typ ypes., V ., Von K n Karma man c n cons nstant nt k =0.4 .4, i , is t the he mo mole lecula lar v viscosity. . T The he p phys ysical c l cons nstraint nt s sho hould ld b be t the he c convergenc nce o of t turbule lent nt f flu luxes a and nd t to bare s soil v l valu lues ( (i.e .e., , , a ., , , and nd d displa laceme ment nt he height ht) w whe hen t n the he a above b bioma mass approache hes z zero. .

 The Noah LSM has a cold bias of around 10 K in the early afternoon of summer over semiarid regions.  The previous efforts to reduce the bias were focused on the tunable parameter by adjusting its value or taking as a function of vegetation height h, e.g. , = (Chen and Zhang, 2009).  However, there is no vegetation height input to the Noah LSM. The derived from the corresponding vegetation height would lead to an overestimation of , suggesting that the problem can’t be fixed by just tuning the parameter and the prescribed also needs to be adjusted, by explicitly applying the physical constraint. Following Zeng and Wang (2007), the bare soil roughness length is taken as 0.01, effective roughness length for momentum is , the maximum Green Fractional Cover is , and the prescribed roughness for momentum is .

 GP GPCP P Pent ntad P Precipitation A n Ana nalys lysis f for pr precipit cipitat atio ion ( (Xi Xie e et a al.,2 l.,2003). .  GH GHCN/CAMS ( (la land nd) T T2m A m Ana nalys lysis f for T2m T2m ( (Fan a n and nd V Van d n den n Do Dool, 2 , 2008). .  NOAA O Optimu mum Int m Interpola lation ( n (OI) I) S SST f for SS SST ( (Reyno ynold lds, 1 , 1988). .  Ano noma maly c ly correla lation i n is u used a as a a me measure o of t the he s ski kills lls f for mo mont nths hs o of M May a y and nd June ne. . References: Fan, Y., and H. van den Dool (2008), A global monthly land surface air temperature analysis for 1948- present, J. Geophys. Res ., 113, D01103, doi: 10.1029/2007JD008470. Xie, P. and Coauthors, 2003: GPCP Pentad Precipitation Analyses: An Experimental Dataset Based on Gauge Observations and Satellite Estimates. J. Climate , 16 , 2197–2214. doi: 10.1175/2769.1.

Experime ment ntal l Cont ntrol May Ma ne June High skill globally for lead 0, decreases with lead 1 (mid-latitude), still maintains good performance over most of the globe, especially over the Nino regions

Experime ment ntal - c l - cont ntrol l No surprise, small May difference in lead 0, Ma initial ocean conditions is the main control and land impact is very small Slightly better over ne June the Pacific mid- latitudes and equatorial Atlantic ocean, still small over the tropics

Cont ntrol Experime ment ntal l May Ma ne June Higher skill and similar patterns in lead 0, decreases substantially in lead 1. As expected, the decrease is relatively small in the Southern Hemisphere (cold)

Experime ment ntal - c l - cont ntrol l May Ma ne June Mixed picture, varies with regions in the N.H., small changes in the patterns with both leads in the S.H.

Experime ment ntal l Cont ntrol CONUS CONUS May Ma ne June Patterns similar to the global, no big difference in lead 0. Skill gain/loss varies with different climate regimes in lead 1

Experime ment ntal - c l - cont ntrol l CONUS CONUS May Ma Better over the northwest Pacific states in lead 1, worse over the east (New England region) ne in both leads June

Cont ntrol Experime ment ntal l May Ma ne June Higher skill than precipitation and close to each other in lead 0 decreases substantially in lead 1 over both hemispheres

Experime ment ntal - c l - cont ntrol l May Ma Main difference in Mid to high latitudes ne June Similar to the global skill, the difference varies with regions

Cont ntrol Experime ment ntal ( l (Z0) CONUS CONUS May Ma ne June Higher skill in lead 0, decreases substantially in lead 1,

Experime ment ntal - c l - cont ntrol l CONUS CONUS May Ma ne June May come from better ocean Better over most of the CoNUS, especially over central great plains in lead 0. Higher skill over the Northwest Pacific and mid-Atlantic regions in lead 1

CONUS CONUS May Ma ne June The skill gain mainly comes from the disagreements between the two configurations

 The new formulations generally lead to a better skill in predicting T2m over the CONUS in the first month and the skill gain/loss varies with different climate regimes for the second month globally.  The changes made to the roughness lengths have a relatively small impact on the precipitation skill, suggesting that the ocean and atmosphere are still the dominant controls over warm season precipitation for relatively short leads.  The impact is also affected by the land-atmosphere coupling strength. The differences mainly show up in the second month due to the coupled nature. An examination of the atmospheric circulation could be very useful.  A careful treatment to land surface parameterization is important to mid-range/seasonal predictions.  More years may be needed to confirm the patterns.