Operational Drought Information System Kingtse Mo Climate Prediction Center NCEP/ NWS/NOAA Operation--- real time, on time and all the time 1

Mission  CPC issues operational monthly and seasonal drought outlook and participates in the Drought Monitor  These products are used by government, NIDIS, local state government, regional centers and private sectors  To support the CPC mission, we give drought briefing each month to review the current conditions and forecasts 2

Definition of drought– persistent dry conditions Colorado basin SE 3

A wet region drought 6 mo running mean black line 3 mo running mean (black line) SM 1-2 months delay No smoothing Red line: monthly mean, no smoothing 75-85W,31-35N 4

SM has much lower freq. over the western region A dry region 5

Good indices 1. They do not depend on season 2. They do not depend on location 3. They are accessible in real time 4. It measures the spatial and temporal scales of drought 5. All indices should be able to pick up strong drought evens 6 Kelly Redmond

Different faces of drought define drought by impact Meteorological drought– P deficit Agricultural drought--- soil moisture deficit Hydrological drought_ runoff or streamflow deficit Using index to define drought 7

SPI fcsts  Recent rain diminishes drought over the Southwest and California  Continuous rainfall events causes floods over the Southeast and the East cast  The SPI24 still shows the strong drought events of 2012 SPI gives the historical development of drought/floods 8

SPI SPI Advantages: Easy to use and only need station data Cover all time scales Do not need a hydrologic model. (Other indices are model derived products) SPI Disadvantages: 1.It does not contain snow information 2.Areas where soil moisture feedback is important or large E, SPI may not be representative (e. g. Amazon) 9

North American Land Data Assimilation system They are not TRUTH Surface land model- Noah, SAC, VIC, Mosaic and Catchment model They are driven by forcing which consists of precipitation (P), Max and min Tsurf and wind speed for a water balance model Some models like Noah and Mosaic have the energetics –radiation terms. VIC has both versions Outputs: Evaporation, Soil moisture, soil temperature, runoff, Snow water equivalent. And many others 10

Don’t worry, be happy!! Even though the total soil moistures differ from one model to another, their anomalies (or percentiles) are very similar!! (Robock et al 2004; Dirmeyer et al. (2004) Koster et al. (2008) All models were driven by the same forcing 15 11

12 Multi model SM information U Washington NCEP/EMC Both captures the wetness over the Eastern and East central United States and dryness over the Southwest and the Plains, But intensity differs

Differences between two systems are larger than the spread among members of the same system The differences are not caused by one model. They are caused by forcing. In general, extreme values from the UW (Green) are larger than from the NCEP (red) ),UW(green) NCEP(red),UW(green) standardized SM anomalies for area 38-42N, W 13

The EMC NCEP system Four models: Noah, VIC, Mosaic and SAC Climatology: On degrees grid P forcing : From the CPC P analysis based on rain gauges with the PRISM correction. (all stations reports within cutoff time Other atmospheric forcing: From the NARR 14

University of Washington system Four models: Noah, VIC, SAC,CLM Catchment (models may have the same name, but versions may not be the same) Climatology: P, Tsurf and low level winds from NOAA/NCDC co-op stations P from index stations 15

Forcing Since the differences among the members of the same system are small, the differences do not come from models. Differences come from forcing. There are two major forcing terms: precipitation and temperature. Their differences are larger after

Experiments  The VIC model of 0.5 degrees resolution from the UW system was chosen for experiments.  All experiments started from Jan using the same initial conditions from the UW VIC model in the UW system.  Experiments end on 31Dec 2008  Forcing terms have two components 1.P forcing :Precipitation 2.F forcing : Tmax, Tmin and wind speed 17

Four experiments  Comparison between Exp (P uw F uw) vs Exp(Pncep,Fuw) and Exp (Puw, Fncep) vs Exp(Pnecp,Fncep) indicates the differences caused by Precip  Comparison between Exp (P uw F uw) vs Exp(Puw,Fncep) and Exp (Pncep, Fuw) vs Exp(Pnecp,Fncep) indicates the differences caused by F forcing (Tsurf and winds) 18

Experiments :RMS differences of SM % Same F forcingSame P forcing 19 Large differences between experiments with the same F forcing but the same P forcing are large

Number of station reports averaged over a year 20

Number of reports /month averaged over the box Large drop in real time 21

Challenges: improving drought monitoring Improve historical and real time Precipitation data and analyses Improve NLDAS model forcing: P, downward short wave radiation etc Improve hydrologic model Improve and integrate satellite observations with station data. Link to attribution 22

NMME/IMME seasonal fcsts 23 We have 6 models: CFSv2 24 members; GFDL, CMC1 and CMC2 : 10 members NASA: 11 members NCAR : 6 members Hindcasts from P, Tsurf monthly means JAS 2013 ASO 2013

Hydroclimate FCSTs SPI forecasts based on the National Multi Model ensemble (NMME) ESP forecasts from the UW Cfsv2_VIC forecasts from the Princeton, EMC and MSU NASA SM from their Coupled model forecasts 24

25 SPI forecast If you have precip monthly mean fcsts, you can have the SPI forecasts Yoon et al. JHM 2012 CGCM

SPI fcsts (201308) 26 verification

ESP (Ensemble streamflow prediction) vs NMME_VIC Fcsts IC s Run VIC with observed P and Tsurf Jan 1,1915 from UW Jan 1, 1979 ESP- P T inputs taken from randomly selected observations Both ESP and NMME_VIC have the same initial conditions, but ESP has no climate forecast information of P and Tsurf Fcst forward Starting date Feb 5Feb 6---  27 NMME_VIC :forcing s were taken from error corrected T P from CGCM

ESP FCST UW ICs= \August lead=1mo Sep lead=2mo Oct lead=3mo Acc ro lead=1mo

SM fcsts EMC_MSU_Princeton 29 AUGUST lead=1mo September lead=2moOct lead=3mo Same as the ESP, but climate forcing is given by the CFSv2 forecasts

NMME_VIC forecasts Initial conditions from the VIC simulation taken from the UW NLDAS_VIC (perfect) Climate forcing derived from the members of the NMME for each model Drive VIC to get SM and Runoff For a given model and given lead time, we took the ensemble mean of all members. The climatology of the forecasts is corrected in the cross validated way. SM /Runoff or SRI3 ensemble mean is the equally weighted mean of all 6 models 30

Fcst skill for SM 31  Lead-1 : correlation >0.8 (WOW!!!)  Lead-3: Over the western interior dry region, the fcsts are still skillful for all seasons and the North Central for January (high skill regions)  Low skill regions are circled

Differences btw NMME-ESP 32 1.No significant differences for Lead-1 and Lead-2 2.Only October and January forecasts pass the Livezey Chen field pass 3. Differences are in the areas that the skill is low and dynamically active areas 4. Oct fcsts are helped by skillful P forecasts Lead-3

Two regimes Dry : western interior & eastern Texas Forecast skill of SM and Runoff are high at lead-3, Contributions are from the initial conditions ESP_VIC also has high skill Areas with low P mean and Low P variability WET: Eastern region and monsoon region Wet areas with large mean P and P variability Skill is low even at Lead-1 Dynamically active and P depends on the moisture transport NMME has higher skill than the ESP 33

Problem with hydroclimate prediction- low P fcst skill 34 No skill after Lead-1 Except the Southeast In Oct When the CF starts to contribute at Lead-2 or higher, the skill of P forecasts are so low, it does not make a difference

Issues of hydroclimate fcsts At Lead-1, the initial conditions dominant the forecast skill. The NMMS precipitation forecasts have some skill, but it competes with the initial conditions At Lead-2 and Lead-3, the impact of forcing starts to contribute to skill, but the skill of P fcsts decreases. In the western region, the Ics still contribute but over the dynamically active region such as the Southeast or the monsoon region, the P forecasts need to be good enough to contribute to SM or RO forecasts at higher lead 35

Conclusions GIVE Me : Better P forecasts at Lead-2 and Lead-3 You will have Better SM and Runoff forecasts over the dynamically active region Give me better station data reporting in real time You will get : better NLDAS with less uncertainties and better forecasts over the dry areas 36

Measure the differences among models R m for a group of models  m : the mean intermodel variance (or spread)  int (m): interannual variance of the ensemble mean Similar formula was used by Dirmeyer et al (2004). to assess Global wetness products except we use variance instead of standard dev

R values for SM % 1.The spread among the members from the same system (UW or NCEP) is small. It is less than 0.4. (Fig. a and b) 2. R values with all UW and NCEP members together is much larger (Fig.c). This implies that the mean differences between two systems are large 38

1.The RMS difference (Fig.d) between the ncep and the UW ensemble SM means are large over the western U. S. (> 20%). 2. Largest differences occur after 2001 as indicated by the mean differences for two periods (Fig. f and g) 39

Prediction  Oceanic conditions ENSO normal the positive SSTAs over the North Pacific will continue through summer.  Precipitation  above normal rainfall over the East  above normal rainfall over the Southwest  Drought All forecasts indicate that drought over the Central U. S. and Texas will improve A normal to slightly above normal monsoon will improve drought conditions over the Southwest 40

RMSE (NMME) R(NMME/ESP) Lead=1mo Lead=2mo Lead=3mo Spread lead=2 and 3 mo  Over the central and western U.S., the ESP has advantages up to lead=2mo  Over the eastern U.S., the NMME has advantage than the esp  For lead=3, skill overall is very low and the NMME and ESP have comparable skill  The spreads are small and located in the area with low skill . Comparison with ESP 41