1. Do Global Models Properly Represent the Feedback Between Land and Atmosphere? An Observational Study from GLACE Paul Dirmeyer 1, Randy Koster 2 and Zhichang Guo 1 1 Center for Ocean-Land-Atmosphere Studies (COLA), Calverton, Maryland, USA 2 NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

2. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL2 GCM inter-comparisons Land’s role in climate Single column model analyses Coupled data assimilation Land-surface model intercom- parisons (in situ) Point validation Global gridded model analyses Large-scale comparison GEWEX Global Land-Atmosphere System Study GLACE is also a component of the CLIVAR Working Group on Seasonal- Interannual Prediction (WGSIP).

3. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL3 GLACE Informing GABLS/GLASS How little we can do to verify coupled land- atmosphere behavior in global weather/climate models Evidence for the need for co-located observations of land (subsurface and surface), atmosphere (near- surface through PBL) and fluxes between them The power of the plural – the value multi-model approaches Things to watch for in this presentation:

4. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL4 Participating Modeling Groups Not all models listed are part of this study. Some are latecomers or did not supply all necessary output fields. InstituteGCMLand Model BMRC - AustraliaBMRCCHASM U. Tokyo - JapanCCSRMATSIRO Env. CanadaCCCmaCLASS COLA – USACOLASSiB CSIRO – AustraliaCSIRO-CC3 & -CC4 NASA/GSFC/CRB – USAGEOS-CRBHySSiB GFDL – USAGFDLLaD Hadley Centre – UKHadAM3MOSES2 SNU – KoreaSNULSM NCAR – USACAM3CLM2 NOAA/NCEP – USAGFSOSU & NOAH NASA/GSFC/GMAO – USANSIPPCatchment UCLA – USAUCLASSiB

5. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL5 Experiment Design All groups integrated their global models for June-August with specified SST. In the control case (W), land surface state variables evolve freely and initial conditions for each ensemble member vary widely (e.g., from 1 June of different years of an AMIP simulation). One ensemble member is used as the source of land state variables to be specified in every member of the test cases… W Simulations: Control integrations - establish a time series of surface conditions (Repeat without writing to obtain simulations W2 –16) 16-member ensembles for June through August time step n Step forward the coupled AGCM-LSM Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES Write the values of the land surface prognostic variables into file W1_STATES time step n+1

6. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL6 Test Cases In case R, all land state variables are replaced at each time step of integration. In case S, only sub-surface soil moisture is replaced. R Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of land surface prognostic variables. S Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of subsurface soil moisture prognostic variables time step n Step forward the coupled AGCM-LSM Step forward the coupled AGCM-LSM Throw out prognostic soil moisture; replace with values from W1_STATES time step n+1 Throw out prognostic soil moisture; replace with values from W1_STATES

7. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL7 All simulations in ensemble respond to the land surface boundary condition in the same way  (coupling strength) is high intra-ensemble variance is small Simulations in ensemble have no coherent response to the land surface boundary condition  is low intra-ensemble variance is large We defined a diagnostic variable Ω that describes the impact of the surface boundary on the generation of precipitation. Diagnostic Analysis Ω = (16σ 2 - σ 2 X ) / 15 σ 2 X, where σ 2 X is the intra-ensemble variance of X and σ 2 is the corresponding variance of the ensemble-mean time series – averaged over six-day intervals.

8. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL8 Global Land-Atmosphere Coupling Experiment Koster, R. D., P. A. Dirmeyer, Z. Guo, G. Bonan, E. Chan, P. Cox, H. Davies, T. Gordon, S. Kanae, E. Kowalczyk, D. Lawrence, P. Liu, S. Lu, S. Malyshev, B. McAvaney, K. Mitchell, T. Oki, K. Oleson, A. Pitman, Y. Sud, C. Taylor, D. Verseghy, R. Vasic, Y. Xue, and T. Yamada, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138-1140. The GLACE project showed that while the 12 participating models differ in their land- atmosphere coupling strengths (the change in , or  between cases S and W), certain features of the coupling patterns are common to many of the models. These features are brought out by averaging over all of the model results.

9. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL9 AridHumid W→ETET→P W2 W1 P ET Arid regime: ET (mostly surface evaporation) very sensitive to soil wetness variations, but the dry atmosphere is unresponsive to small inputs of water vapor. Humid regime: Small variations in ET affect the conditionally unstable atmosphere (high moist static energy), but deep-rooted vegetation (transpiration) is not responsive to nominal soil wetness variations. Coupled Feedback Loop In between, soil wetness sensitivity and conditional instability both have some effect.

10. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL10 The Current Study We have, in the results of GLACE, a multi-model-based estimate of the strength and spatial variation of land- atmosphere coupling, and its relationship to state variables and fluxes within global models. Can we confirm or refute the GLACE results using the observational record?

11. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL11 The Observational Quandary Three major impediments to validating the GLACE results: The parameter  is a handy construct for model comparisons and analysis, but  is not a physical quantity. It is an artifact of ensemble model simulations. There is no direct way to calculate a field of , never mind , from observations. It is very difficult to infer feedbacks from the observational record. This is one of the main reasons we use models, where we can control the parameters of experiments, generate very large sample sizes for statistical testing and separate signal from noise. We lack global measurements of soil moisture & surface fluxes, which are key elements of the coupling pathway. Thus, at best, we can only validate the behavior of global models over a small number localities.

12. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL12 What in situ Data Are There? In order to compare the model representation of land- atmosphere coupling strength to the real world, we need: Complete observations of land surface state variables, near surface atmospheric states, and fluxes between land and atmosphere. A long enough period of record to provide a large sample that both spans the range of variability of these variables and provides for adequate statistical significance of the results. Data in the same season as the GLACE experiments: June, July and August. There are very few sources of observational data that can meet all these requirements. Two are identified for this study.

13. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL13 ARM/CART DOE operates the Atmospheric Radiation Measurement (ARM) program; in particular, the Southern Great Plains site consists of a Central Facility and a number of Extended Facilities Elevation (m MSL) across a large area of Oklahoma and southern Kansas (map at right - nine stations have sufficient data for comparison with the models). Kansas Oklahoma

14. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL14 FLUXNET The FLUXNET network of micrometeorologic al tower sites (table right). We have drawn upon the long-term archive at the Oak Ridge National Laboratory DAAC. European sites do not measure soil moisture – of limited use. FLUXNET Sites LatitudeLongitudeSurface Bondville40.006 N88.292 W Corn/soybean rotation Little Washita34.960 N97.979 W Grass, rangeland Bayreuth50.161 N11.882 E Needleleaf evergreen Hyytiala61.847 N24.295 E Needleleaf evergreen Loobos52.168 N5.744 E Needleleaf evergreen Tharandt50.964 N13.567 E Needleleaf evergreen

15. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL15 Closed Energy Balance FLUXNET ARM/CART No GHF

16. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL16 Coupling Strength ~ Goodness of Fit For the coupling of soil moisture to ET, we hypothesize that the goodness of fit of a curve relating soil wetness and evaporative fraction should be proportional to ∆  NLH (the change in coherence of normalized latent heat flux (NLH) from case W to case S). The goodness of fit parameter g = s/R where: Best fit through 20 bins ( i ) with equal number of points (blue lines in next slide). Range in y of the best fit. Make no a priori assumption about the functional relationship of NLH on SW. Low g means good fit, high g poor fit. NLH = LH/NetRad

17. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL17 Goodness of Fit (Case W) NLH vs. SW for nine models for the ARM Central Facility. The points are 6- day means from all ensemble members. The red dots are for the member of W chosen as the basis for the test cases R and S. The relationship from observations is above. Globally r 2 for ∆  NLH vs. g(NLH) = 0.33 but r 2 for ∆  LHF vs. g(LHF) = 0.53 g=0.319 g=0.102g=0.058 g=0.247g=0.151g=0.221 g=0.280 g=0.855g=0.194 g=0.201

18. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL18 Models vs. Observations OBS CCCma COLA CSIRO- CC3 GEOS- CRB GFDL HadAM3 CAM3 GFS/ OSU NSIPP M.M. Bondville g(NLH,SW) 0.28 0.150.520.090.060.500.200.280.240.33 g(LHF,SW) 0.350.240.130.470.120.140.410.200.260.460.26 SW mean0.760.220.520.150.390.440.390.010.140.270.26 SW range0.570.590.910.141.120.990.400.050.31 0.57 LHF mean1011191371221211171226389149105 Little Washita g(NLH,SW) 0.210.320.210.960.130.060.350.240.220.140.27 g(LHF,SW) 0.290.250.181.170.110.090.300.270.200.120.26 SW mean0.350.030.600.01 0.110.220.020.030.230.13 SW range0.290.260.740.070.760.990.470.070.080.480.43 LHF mean5769728727428588407859 ARM Average g(NLH,SW) 0.200.320.190.860.100.060.280.220.250.150.34 g(LHF,SW) 0.430.250.190.990.090.100.280.290.220.150.25 SW mean0.370.030.710.030.020.210.290.020.040.290.17 SW range0.340.260.560.101.021.000.440.060.120.530.45 LHF mean1046980753668951084210968

19. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL19 Relative Relationships There is a tendency for the models to link soil wetness more strongly (smaller g ) to LHF than to NLH. Furthermore, observations show g for SHF is less than g for NSH, and SHF links to soil moisture more strongly than LHF does, consistent with Betts (2004). g(*,SW) LHFSHFNLHNSH Coldwater 0.3130.2830.2470.448 Cordell 0.3500.2820.2840.310 Elk Falls 0.3300.3680.3160.298 El Reno 0.7630.5230.6400.652 Hillsboro 0.5080.5140.3530.848 Lamont 0.4960.4720.4380.476 Meeker 0.3300.1990.1970.193 Morris 1.0570.6310.5630.844 Pawhuska 0.3390.1730.2150.451 Bondville0.3540.4190.2850.705 Little Washita0.2940.1990.2140.170

20. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL20 Models Don’t Behave Like Obs. Models rarely have these attributes. Obs show better fit for NLH than LHF, only 3½ models do. Obs show better fit for SHF than LHF, 1 model does. Obs: SHF has better fit than NSH, only 1½ models do. g(*,SW) LHFSHFNLHNSH Obs (ARM)0.4270.2320.2010.248 CCCma0.2530.3370.3190.342 COLA0.1860.2600.1940.204 CSIRO-CC30.9880.7430.8551.045 GEOS-CRB0.0850.1760.1020.118 GFDL0.1030.1510.0580.126 HadAM30.2840.3710.2800.308 CAM30.2910.3350.2210.228 GFS/OSU0.2230.2950.2470.245 NSIPP0.1460.2190.1510.153

21. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL21 Why the Differences Between Models and Obs? One possible explanation is that the GCMs emphasize a different factor controlling surface heat flux than does the real world. For example the Penman-Monteith equation and similar relationships have two main terms; One based on potential evapotranspiration (effectively net radiation) One based on the humidity gradient between the land surface and near-surface air. We lack complete information (namely aerodynamic resistance) that would allow us to directly compare the relative magnitudes of each term for each model and for observations. We can, however, compare the main components of each term among the models and observations.

22. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL22 Cause of Model Behavior All of the models suffer from a tendency to simulate excessively warm temperatures and unrealistically low daytime relative humidity at least over the ARM region. Categorical frequency of occurrence of net radiation (top), the difference between actual and saturation specific humidity (middle) and temperature (bottom) over the ARM region for observations (bars), and the mean of the GCMs (markers). Vertical lines span the range of models for each bin. Net Radiation Temperature q Deficit

23. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL23 Reversed Relationship Is Global The stronger dependence on soil wetness of latent heat than evaporative fraction predominates in models over most of the globe (blue areas in map of multi-model g(LHF,SW)/g(NLH,SW) below). All models have a global mean value of this ratio <1, and 7 of 9 models have a majority of the land surface covered by values <1. Thus, according to this analysis, most models appear to have a “reversed” relationship between soil wetness and surface fluxes – in contrast to nature, soil moisture in models appears to be tied more strongly to evaporation than to evaporative fraction. ARM Site

24. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL24 Betts Analysis Betts (2004) found a strong relationship between surface properties and lifting condensation level (LCL) in ERA40. GLACE model relationships vary – the table on the next slide shows r 2 between SHF and LCL and estimated mean PBL heating rates. European FLUXNET sites are included since soil wetness is not needed for these calculations.

25. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL25 Betts Analysis OBS (SHF) OBS (SHF+GHF ) BMRC CCCma COLA CSIRO- CC3 GEOS- CRB GFDL HadAM3 CAM3 GFS/ OSU NSIPP Model Average ARM r2r2 0.590.630.030.700.720.210.370.740.650.780.360.490.51 Htg rate3.74.10.76.13.21.95.23.72.13.22.83.33.2 Bond- ville r2r2 0.270.220.030.580.890.600.550.730.700.690.570.000.53 Htg rate4.14.8-0.43.94.92.23.63.23.13.4 3.0 Little Washit a r2r2 0.590.650.030.700.580.160.370.720.690.760.370.530.49 Htg rate2.94.10.76.12.51.65.74.02.73.73.23.63.4 Bayreu th r2r2 0.400.510.010.310.090.520.00 0.600.610.180.150.25 Htg rate5.8 0.45.8-2.02.24.03.95.33.52.9 Hyytial a r2r2 0.55N/A0.460.300.610.810.580.600.760.580.250.360.53 Htg rate4.5N/A3.74.98.64.17.75.45.77.06.27.36.1 Loobo s r2r2 0.510.570.270.380.630.390.460.320.650.620.210.170.41 Htg rate6.07.2-5.37.64.03.56.810.73.25.37.8-14.2.9 Tha- randt r2r2 0.390.490.010.660.490.380.000.230.450.610.140.150.31 Htg rate3.13.70.47.53.62.25.52.93.44.33.53.7 Models usually underestimate the strength of the relationship between SHF and LCL. PBL heating rates are rarely within 0.5° of observed mean rates. Low values of r 2 suggest models that do not represent the relative importance of SHF as a source of boundary layer heating (or cooling) compared to other thermodynamic processes.

26. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL26 LCL vs. Soil Wetness Observed ARM relationship agrees with Betts’ theory of soil wetness controls on SHF. The models are all over the place.

27. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL27 W2 W1 P ET Coupled Feedback Loop Everything so far has concerned the terrestrial branch of the loop – what about observational validation of the behavior of GLACE models’ precipitation?

28. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL28 Links to Precipitation Potential evidence for land-atmosphere coupling over the central U.S. has been found in the observational record of precipitation, based on lagged autocorrelation of pentad precipitation (map below; Koster et al. 2003) and categorical monthly precipitation (Koster & Suarez 2004). Is there a similar relationship in the GLACE models? July 1 166102126 Precipitation

29. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL29 Pentad Model Precipitation Averaged over the conterminous U.S. and grouped by month, some models, especially the multi- model average, show a magnitude and time evolution of lagged auto-correlation similar to observations (dotted lines).

30. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL30 Month-to-Month Persistence (Koster & Suarez 2004) showed a tendency for persistence of anomalous precipitation in NH mid-latitudes that using pentile rankings (wettest 20% of months were usually followed by wet months, etc.). We repeat the investigation with quartiles (right) and find the models are slightly weaker than observations at showing persistence of wettest (purple) and driest (hatched) 25% of cases. Other influences (e.g. SST impacts) may also play a role.

31. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL31 Summary There exist few locations with long records of observations of the necessary data to verify weather and climate models’ coupling behavior between land and atmosphere. In these locations, GCMs show stronger dependence of LHF on soil moisture than observations suggest, and weaker links to SHF or evaporative fraction. Systematic errors in surface temperature and humidity may contribute to the incorrect dependencies. These problems may also lead to excessive boundary layer growth and incorrect PBL heating rates. Nevertheless, the models (averaged together) capture observed lagged relationships of monthly rainfall.

32. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL32 Summary GLACE results cannot be disproved by the poor validation of individual models, but there is certainly room for improvement in the parameterization of model “physics”. The multi-model approach is further supported by the results of this validation study – the multi-model mean performs better than most models in all circumstances, and is often best. Long-term co-located measurements of soil wetness, surface fluxes and near-surface meteorology should be distributed around the globe in order to aid model development and assess the potential for SW as a predictor for climate via land- atmosphere feedback.

33. 20 Sep 2005Dirmeyer - Joint GABLS/GLASS Workshop - DeBilt, NL33 Thank you! This work was conducted under support from National Aeronautics and Space Administration grant NAG5-11579.