FORECAST UNCERTAINTY UNDER

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1 FORECAST UNCERTAINTY UNDER DIFFERENT LAND-ATMOSPHERE COUPLING REGIMES Hyo-Jong Song and Craig R. Ferguson Atmospheric Sciences Research Center, University at Albany, SUNY, Albany, NY

2 BACKGROUND Takeaways from the Global Land Atmosphere Coupling Experiments GLACE-1: coupling could directly affect a model s ability to simulate climate predictability. GLACE-2: realistic soil moisture initialization could improve sub-seasonal 2 m air temperature forecasts for limited areas. GLACE-1/2: the sign of the coupling feedback and its absolute sensitivity (especially, of precipitation to soil wetness), including its variability as a function of surface and atmospheric state, is still UNQUANTIFIED.

3 MOTIVATION AND OBJECTIVE Key Questions: (1)What is the distinctive contrast in precipitation forecast performance under different coupling regimes? (2) What are the source sensitivities driving this behavior? Objectives: (1) Observe the contrast in forecast skill for convection system during wetand dry-coupling regimes. (2) Investigate how these errors are linked to land state (Soil Moisture, SM).

4 APPROACH (1) Perform daily classification of dry/wet-coupling regime using CFSR in CTP(convective triggering potential)-hi(humidity index)-sm(soil moisture) space following Roundy et al. (2013) (2) Select wet/dry cases with selection criteria: filled with 75% w/i Southern Great Plains (SGP) domain AND 65% w/i parent domain WRF DOMAIN Dry-Coupling Regime JUL 2006 (14days) Wet-Coupling Regime JUN 2007 (14days) SGP DOMAIN (75%) PARENT DOMAIN (65%) DRY WET (3) Use WRF-Noah (V3.6) to perform short-term (30hr) simulations and compare against best estimate truth (NARR and NLDAS-2-Noah) for SGP domain.

5 Model 12km spatial/ 43-levels vertical IC/BC: 3hrly NARR 32km/ 30 levels MODEL AND FORECAST CONFIGURATION Ferrier MP, RRTMG RAD, Monin-Obukov Surface Layer, MYJ PBL, Noah LSM, BMJ CONV Time sequencing Forecast strategy: 30-hr forecast from every -18LT (SGP Local Time) using I.C. & B.C. from NARR and early 6hr discarded for spin-up 06LT 12LT (Day -1) 12LT (Day 0) (spin-up) 06LT 12LT (Day 0) 12LT (Day 1) (spin-up)

6 NLDAS-2 WRF DIURNAL CYCLE IN SGP DOMAIN Event-averaged Diurnal Cycle Dry-Coupling: Dry bias in SM Wet-Coupling: Wet bias in SM PM AM +03LT-peak precipitation diurnal cycle of wet-coupling regime is distorted as around -12LT-peak in WRF-Noah simulation, while 00LT-peak of dry-coupling regime as -06LT-peak. Question: are these discrepancies related to initial SM (IC SM) bias, dry bias in dry-coupling regime and wet bias in wet-coupling regime?

7 BIAS IN ATMOSPHERIC CONVECTION The timings of shallow convection in drycoupling regime and deep convection in wetcoupling regime are consistent with the precipitation peak of NLDAS-2. The downward motions of WRF bias (WRF-NARR, shading) around peak time under both coupling regimes well correspond to precipitation biases. Are these biases in convection and precipitation linked to initial SM (IC SM) bias? DRY-C bias IC SM WET-C bias IC SM

8 DEPENDENCY OF SURFACE FLUX VARIABILITY ON SM UNDER DIFFERENT COUPLING REGIMES NLDAS-2 (99%-Significant* Regression Coefficient) WRF (99%-Significant* Regression Coefficient) Dry-coupling regime: LHF shows strong dependency on the SM (947.71W/m2) in contrast to SHF ( W/m2) in NLDAS-2. This discrepancy is also observed in the result of WRF- Noah with exaggerated sensitivity (LHF: W/m2, SHF: W/m2). Wet-coupling regime: Regression coefficient of NLDAS-2 sensible heat flux ( W/m2) is larger in absolute value than that (447.74W/m2) of NLDAS-2. WRF-Noah shows the larger difference b/w SHF and LHF (SHF: W/m2, LHF: W/m2). 0-10cm SM (m3/m3) at -12LT

9 NLDAS-2 (99%-Significant* Regression Coefficient) DEPENDENCY OF LAND SURFACE VARIABILITY ON SM 2-m specific humidity (Q2) of dry-coupling regime shows strong dependency on the ontime SM (32.14g/kg) in contrast to that of wetcoupling regime (4.32g/kg) in NLDAS-2. This discrepancy is also observed in the result of WRF-Noah with exaggerated sensitivity (DRY-C: 39.08g/kg, WET-C: 7.60g/kg). WRF (99%-Significant* Regression Coefficient)

10 WET-C DRY-C Td ANOMALY CORRELATION WITH SM AT -12LT NARR (line, shading:99%-significance) T Td WRF (line, shading: 99%-Significance) T In dry-coupling regime, IC SM variability looks mainly contributing to thermodynamic structures by latent heat flux. In wet-coupling regime, it seems mainly modulating thermodynamic structures by sensible heat flux.

11 Td 00LT (NARR) T COMPOSITE ANALYSIS BASED ON SM AT -12LT: DRY-COUPLING Average(DRY-C) Drier(<-1sigma) Wetter(>+1sigma) CAPE 00LT (WRF) In the group of drier IC SM (<-1sigma) of NARR, reduced latent heat flux (LHF) makes LCL higher (Plcl=735hPa) than that of climatology (752hPa) and CAPE (1133J) smaller than 1900J. In the group of wetter IC SM (>+1sigma) of NARR, increased LHF makes LCL lower (794hPa) and CAPE larger (2156J). Thermodynamic variability of dry-coupling regime looks mainly controlled by latent heat flux, which is captured by WRF-Noah.

12 Td 00LT (NARR) T COMPOSITE ANALYSIS BASED ON SM AT -12LT: WET-COUPLING Average(WET-C) Drier(<-1sigma) Wetter(>+1sigma) CAPE 00LT (WRF) In the group of drier IC SM (<-1sigma) of NARR, increased heat flux makes the lowest-level T (~29C) larger than that of climatology (~28C) and CAPE (2939J) larger than 2392J. In the group of wetter IC SM (>+1sigma) of NARR, decreased heat flux makes the lowest-level T (~27C) smaller and CAPE (1752J) larger. Thermodynamic variability of wet-coupling regime seems mainly controlled by sensible heat flux, which is unclear in WRF result.

13 ANOMALLY CORRELATION WITH INITIAL SM (99%-SIGNIFICANCE) WRF NARR Anomaly correlation of LCL-PBLH with IC SM shows well-featured contrast, which helps explain the opposite-signed CAPE responses to IC SM under different regimes consistent with composite analysis results. DRY-C IC SM unfavorable to CONVECTION, WET-C IC SM unfavorable to CONVECTION But, in wet-coupling regime the anomaly correlation of LCL-PBLH in WRF-Noah forecast has lower significance than that of NARR.

14 SUMMARY AND DISCUSSION Where does the greatest contrast in precipitation forecast skill exist under L-A coupling? +03LT-peak precipitation diurnal cycle of wet-coupling regime is distorted as around -12LT-peak in WRF-Noah simulation, while 00LT-peak of dry-coupling regime as -06LTpeak. The biases of WRF-Noah in convection structures well correspond to these precipitation biases. In dry-coupling regime, the climatological convection of NARR and the bias of WRF- Noah are concentrated on relatively lower levels, while In wet-coupling regime, those exist overall through troposphere. How these errors are linked to initial SM (IC SM) bias? Dry-Coupling Regime: SM LCL-PBLH, CAPE ( Atmospheric Convection ) at precipitation peak time controlled by Latent Heat Flux (less sensitive to SHF) Wet-Coupling Regime: SM LCL-PBLH, CAPE ( Atmospheric Convection ) at precipitation peak time controlled by Sensible Heat Flux (less sensitive to LHF) Both responses in thermodynamic structure can be used to explain contribution of IC SM bias to the bias of atmospheric convection in the precipitation peak time.

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