## 6 Conclusions
Advantages for an increase accuracy in monitoring and prediction at the surface can be anticipated to be sizeable. An Earth system model which is capable of reproducing the observed range of surface temperatures in accordance with the amplitude of the diurnal and the seasonal cycle, and which reproduces synoptic disturbances or persistent weather, will have greater skill in predicting temperature extremes and therefore increase the relevance for climate studies. Similarly, the water cycle can benefit from more realistic precipitation and evaporation rates when approaching scales that are relevant to convection. An accurate and up-to-date land-use description is necessary for obtaining modelled soil moisture that is more directly comparable to kilometer scale remote-sensing products. Attention to the energy and water cycles realism is a crucial ingredient to support simulations of the natural and anthropogenic carbon exchanges at the surface. Representing the anthropogenic influence on the Earth surface and its effects within Earth system models is a challenge for the next generation of models. In these models, there will be an increased role for Earth observations to map and monitor the natural and human-driven surface changes in the Earth system. Their aim will be to integrate weather, climate and environmental applications. This review paper is the result of a collective effort from all authors. G.B. looked after the structure and coordination of the review and the introductions to each section. A.B. (Anton Beljaars) contributed to the sections' harmonisation. The authors contributions clustered by topic are indicatively as follows: On EO insitu and satellite datasets M.D. and M.F.C. for surface networks; C.L., I.F.T. and R.O. on LST satellites; J.M.-S. and Y.K. on L-band satellites. On ESM aspects: P.A.D. and X.Z. on soil; A.A.-P., P.G., J.M. and S.B. on carbon/vegetation ; G.A. and E.N.D. on snow and land cryosphere; I.S. and N.W. on drag; H.H., J.B., K.M., S.P.E.K. and S.T. on ocean and marine cryosphere; C.A., F.P., H.C. and R.U. on soil hydrology; M.C. and R.I.W. on lakes. On assimilation aspects B.R., F.C., J.-F.M., N.B., P.D.R., R.R. and S.K. On EO ESM research transfer A.B. (Andy Brown), C.B., F.R., R.B., M.E., M.D., S.I.S. and S.M. All authors have substantially contributed to the critical thinking, text redaction and/or figures preparation. Funding from the European Union CO2 Human Emissions H2020 project grant agreement No 776186 (Nicolas Bousserez, Margarita Choulga, Joe McNorton) and United States NOAA-PMEL contribution No 4808 (Meghan F. Cronin) are acknowledged. The authors wish to thank the supporting institutes of the respective affiliation. Adrian Simmons and Alan Betts are thanked for their precious advice and review of early versions of this manuscript. We thank three anonymous reviewers and the assigned Remote Sensing editors for their valuable comments. At ECMWE, Georg Lentze, Anabel Bowen and Simon Witter editorial help and assistance have been greatly appreciated. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
AIRS Atmospheric InfraRed Sounder  
AMSR-E Advanced Microwave Scanning Radiometer for EOS (Earth Observing System)  
AMSR-2 Advanced Microwave Scanning Radiometer 2  
AMSU-A Advanced Microwave Sounding Unit-A  
AROME Action de Recherche Petite Echelle Grande Echelle  
ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer  
ATSR Along Track Scanning Radiometers  
AVHRR Advanced Very High Resolution Radiometer