SUMMARY AND OUTLOOK

Coupling, variability and memory are identified as the ingredients of land-atmosphere interactions that can contribute to enhanced predictability in Earth system models. Progress in the

understanding and simulation of key processes in these areas would be facilitated by an improved and expanded observational capability to characterize these properties for different biomes, land-use situations and climate regimes. We are on the verge of having sufficient observational data to begin to address these problems, although scale differences persist. Furthermore, a consistent set of diagnostics and metrics that can highlight the difference between the model and the real-world will further advance the potential for enhanced land surface contribution to prediction skill.

The memory inherent in land surface water reservoirs such as soil, snow and lakes can extend the quality of medium range prediction beyond the classic deterministic limits, especially in the

presence of significant pre-existing anomalies. However, this ingredient alone is not sufficient without the other two components (realistic coupling and sufficient variability), which demands

evaluating models over extensive periods of time such as those covered by modern era reanalyses and the period of continuous satellite remote sensing. Predictability can be converted to forecast skill with high-quality land surface initial conditions consistent with the land surface model (cf.

Koster et al. 2009). Innovations need to be incorporated into operational prediction models in a timely fashion. We are at an exciting time when major progress in exploiting the predictability in land- atmosphere feedbacks is likely in the coming decade.

REFERENCES

Abramowitz, G., 2005: Towards a benchmark for land surface models, Geophysical Research Letters, 32, L22702, doi: 10.1029/2005GL024419.

Abramowitz, G., 2012: Towards a public, standardized, diagnostic benchmarking system for land surface models. Geoscientific Model Development, 5, 819-827,

doi:10.5194/gmd-5-819-2012.

Balsamo, G., R. Salgado, E. Dutra, S. Boussetta, T. Stockdale and M. Potes, 2012: On the

contribution of lakes in predicting near-surface temperature in a global weather forecasting model, Tellus-A, 64, 15829, doi: 10.3402/tellusa.v64i0.15829.

Betts, A.K., R. Desjardins, D. Worth, S. Wang and J. Li, 2014: Coupling of winter climate transitions to snow and clouds over the Prairies. Journal of Geophysical Research - Atmos., 119, 1118-1139, doi: 10.1002/2013JD021168.

Boone, A., and co-authors, 2004: The Rhône-aggregation land surface scheme intercomparison project. Journal of Climate, 17, 187-208.

Dirmeyer, P.A., 1994: Vegetation stress as a feedback mechanism in mid-latitude drought. Journal of Climate, 7, 1463-1483.

Dirmeyer, P.A., 2001: Climate drift in a coupled land-atmosphere model. Journal of

Hydrometeorology, 2, 89-100, doi: 10.1175/1525-7541(2001)002<0089:CDIACL>2.0.CO;2.

Dirmeyer, P.A., C.A. Schlosser and K.L. Brubaker, 2009: Precipitation, recycling, and land memory:

An integrated analysis. Journal of Hydrometeorology, 10, 278-288.

Ek, M.B. and A.A.M. Holtslag, 2004: Influence of Soil Moisture on Boundary Layer Cloud Development. Journal of Hydrometeorology, 5, 86-99. doi:

10.1175/1525-7541(2004)005<0086:IOSMOB>2.0.CO;2.

Fennessy, M.J. and J. Shukla, 1999: Impact of initial soil wetness on seasonal atmospheric prediction. Journal of Climate, 12, 3167-3180.

Gentine, P., A.A.M. Holtslag, F. D'Andrea and M. Ek, 2013: Surface and atmospheric controls on the onset of moist convection over land. Journal of Hydrometeorology, 14, 1443-1461, doi: 10.1175/JHM-D-12-0137.1.

Guo, Z., P.A. Dirmeyer and T. DelSole, 2011: Land surface impacts on subseasonal and seasonal predictability. Geophysical Research Letters, 38, L24812, doi:10.1029/2011GL049945.

Guo, Z., P. A. Dirmeyer, and T. DelSole, and R. D. Koster, 2012: Rebound in atmospheric predictability and the role of the land surface. Journal of Climate, 25, 4744-4749, doi: 10.1175/JCLI-D-11-00651.1.

Gupta, H.V., L.A. Bastidas, S. Sorooshian, W.J. Shuttleworth and Z.L. Yang, 1999: Parameter estimation of a land surface scheme using multicriteria methods. Journal of Geophysical Research, 104, 19491-19503.

Koster, R.D., Z. Guo, P.A. Dirmeyer, R. Yang, K. Mitchell and M.J. Puma, 2009: On the nature of soil moisture in land surface models. Journal of Climate, 22, 4322-4335,

doi: 10.1175/2009JCLI2832.1.

Koster, R. D. and co-authors, 2011: The second phase of the Global Land-Atmosphere Coupling Experiment: Soil moisture contributions to subseasonal forecast skill. Journal of

Hydrometeorology, 12, 805-822, doi: 10.1175/2011JHM1365.1.

Lo, M.-H. and J.S. Famiglietti, 2010: Effect of water table dynamics on land surface hydrologic memory. Journal of Geophysical Research, 115, D22118.

Lowman, L.E.L. and A.P. Barros, 2014: Investigating links between climate and orography in the central Andes: Coupling erosion and precipitation using a physical-statistical model. Journal of Geophysical Research 119, 1322-1353, doi: 10.1002/2013JF002940.

Mariotti, A., S. Schubert, K. Mo, C. Peters-Lidard, A. Wood, R. Pulwarty, J. Huang and D. Barrie, 2013: Advancing drought understanding, monitoring, and prediction. Bulletin of the American Meteorological Society, 94, ES186–ES188, doi: 10.1075/BAMS-D-12-00248.1.

Ozdogan, M., M. Rodell, H.K. Beaudoing and D.L. Toll, 2010: Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data. Journal of Hydrometeorology, 11, 171-184. doi: 10.1175/2009JHM1116.1.

Pielke Sr., R.A., A. Pitman, D. Niyogi, R. Mahmood, C. McAlpine, F. Hossain, K. Goldewijk, U. Nair, R. Betts, S. Fall, M. Reichstein, P. Kabat, and N. de Noblet-Ducoudré, 2011: Land use/land cover changes and climate: Modeling analysis and observational evidence. WIREs Climate Change 2011, 2:828–850. doi: 10.1002/wcc.144.

Santanello J.A., C.D. Peters-Lidard, M.E. Garcia, D.M. Mocko, M.A. Tischler, M.S. Moran and D.P.

Thoma, 2007: Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed, Remote Sensing of Environment, 110 79-97, doi: 10.1016/j.rse.2007.02.007.

Santanello J.A., S.V. Kumar, C.D. Peters-Lidard, K. Harrison and S. Zhou, 2013: Impact of Land Model Calibration on Coupled Land-Atmosphere Prediction. Journal of Hydrometeorology, 14, 1373–1400. doi: 10.1175/JHM-D-12-0127.1.

Schlosser, C.A., and P.C.D. Milly, 2002: A model-based investigation of soil moisture predictability and associated climate predictability. Journal of Hydrometeorology, 3, 483-501.

Schmid P. and D. Niyogi, 2013: Impact of city size on precipitation- odifying potential, Geophysical Research Letters, 40, doi: 10.1002/grl.50656.

Seneviratne, S.I., D. Lüthi, M. Litschi and C. Schär, 2006: Land-atmosphere coupling and climate change in Europe. Nature, 443, 205-209.

Seneviratne, S.I. and R.D. Koster, 2012: A revised framework for analyzing soil moisture memory in climate data: Derivation and interpretation. Journal of Hydrometeorology, 13, 404-412, doi: 10.1175/JHM-D-11-044.1.

Stap, L.B., B.J.J.M. van den Hurk, C.C. van Heerwaarden and R.A.J. Neggers, 2014: Modeled contrast in the response of the surface energy balance to heat waves for forest and grassland. Journal of Hydrometeorology, 15, 973-989, doi: 10.1175/JHM-D-13-029.1.

Tawfik, A.B., and P.A. Dirmeyer, 2014: A process-based framework for quantifying the atmospheric preconditioning of surface triggered convection. Geophysical Research Letters, 41,

173-178, doi: 10.1002/2013GL057984.

Taylor, C.M., R.A.M. de Jeu, F. Guichard, P. P. Harris and W.A. Dorigo, 2012: Afternoon rain more likely over drier soils. Nature, 489, 423-426, doi: 10.1038/nature11377.

Teuling, A.J. and co-authors, 2010: Contrasting response of European forest and grassland energy exchange to heatwaves. Nature Geoscience, 3, 722-727.

van Heerwaarden, C.C., J. Vilà-Guerau de Arellano, A.F. Moene and A.A.M. Holtslag, 2010:

Interactions between dry-air entrainment, surface evaporation and convective boundary layer development. Quarterly Journal of the Royal Meteorological Society, 135, 1277-1291, doi:10.1002/qj.431.

van Heerwaarden, C.C., J.P. Mellado and A. De Lozar, 2014: Scaling laws for the heterogeneously heated free convective boundary layer. Journal of Atmospheric Sciences, 71, 3975-4000, doi: 10.1175/JAS-D-13-0383.1.

Vinnikov, K. Ya. and I.B. Yeserkepova, 1991: Soil moisture, empirical data and model results.

Journal of Climate, 4, 66-79.

Viterbo, P. and A.K. Betts,1999: Impact of the ECMWF reanalysis soil water on forecasts of the July 1993 Mississippi flood. Journal of Geophysical Research, 104, 19361-19366.

World Climate Research Programme, 2011: WCRP Workshop on Drought Predictability and Prediction in a Changing Climate. WCRP Report 21/2011, 33pp.

Wulfmeyer, V. and co-authors, 2011: The Convective and Orographically-induced Precipitation Study (COPS): The scientific strategy, the field phase, and research highlights. Quarterly Journal of the Royal Meteorological Society, 137, 3-30, doi: 10.1002/qj.752.

Xu, L. and P. Dirmeyer, 2013 Snow-atmosphere coupling strength. Part II: Albedo effect versus hydrological effect. Journal of Hydrometeorology, 14, 404-418,

doi:10.1175/JHM-D-11-0103.1.

Zaitchik, B.F., J.A. Santanello, S.V. Kumar and C.D. Peters-Lidard, 2013: Representation of soil moisture feedbacks during drought in NASA Unified WRF (NU-WRF). Journal of

Hydrometeorology, 14, 360-367, doi: http://dx.doi.org/10.1175/JHM-D-12-069.1.

______