# A Review of Machine Learning Applications in Land Surface Modeling
## 2 LSMs: Importance, Then and Now  
As mentioned in the introduction, LSMs are numerical models that simulate land surface processes, such as absorption and partitioning of radiation, water, and carbon between the land surface and atmosphere. Provided with meteorological forcing as inputs (from an atmospheric model either in 'coupled' mode or an 'uncoupled' mode), they estimate latent heat fluxes (LH), sensible heat fluxes (SH), carbon fluxes, surface runoff, deep drainage, reflected solar and emitted longwave radiation as output [10; 11]. While LH and SH control the boundary layer properties and precipitation; net carbon flux influences the atmospheric CO\({}_{2}\) content. These estimates play a critical role in determining the effects of human-modified land surface and human emissions on changes in the climate. LSMs are perhaps the most efficient tools to predict how the continuously evolving earth surface will modify the hydroclimate in coming years and centuries. The extents of modeling activities with LSMs include multiple interlinked disciplines (such as atmospheric modeling, crop modeling, and hydrologic modeling) relevant to this overarching problem. LSMs were originally developed by the atmospheric modeling community who needed physical boundary conditions consisting of energy and moisture partitioning, albedo, and surface roughness to indicate the impact of the surface on the atmospheric processes. [PERSON] [12], in 1922, first mentioned the importance of stomatal conductance on weather processes. Early studies, such as [PERSON] et al. [13] used albedo as a proxy for vegetation and started investigating the effects of deforestation in terms of it. Starting from the 1980s, scientists started understanding the land surface-atmosphere interactions [14; 15]. [PERSON] et al. [16] summarized the importance of land surface in climate modeling in a review paper. He discussed different boundary layer schemes and the results from global climate model (GCM) sensitivity studies using these schemes. He concluded that the regional and global climate is significantly influenced by albedo, surface moisture and roughness, and the inclusion of vegetation. However, till then, it was not clear how much spatial detail of the surface is sufficient to accurately represent the lower boundary conditions. For that decade, improvements of LSMs were driven by the need to understand the effects of deforestation in various parts of the world. In the 2000s, scientists started to visualize the importance of land in the context of sub-seasonal to seasonal forecasting. The land surface was identified as a slowly varying component of the earth system, which has a major role in modulating the atmospheric response at a longer timescale than weather prediction. [PERSON] et al. papers [7; 17], in connection with the Global Land-Atmosphere Coupling Experiment (GLACE), identified soil moisture as an important factor altering evaporation and precipitation. They also highlighted the regions where strong coupling between soil moisture and precipitation exists. For the first time, they introduced the concept of 'coupling strength' to quantify such coupling, which is still being widely used in land-atmosphere interaction studies. However, while modeling these interactions, there exists a huge variation among the global models, attributable to the uncertainties in terrestrial and atmospheric branches, and the models fail to represent the land-surface coupling accurately [18]. Specifically, they found systematic biases in near-surface temperature, humidity, and precipitation, which contribute to the uncertainty. [PERSON] et al. [19] summarized the findings related to soil moisture-precipitation relations in a review paper and concluded that the relationship between soil moisture and precipitation is evident in observations and models. However, significant uncertainty remains in quantifying those in terms of the strength of coupling, and persistence characteristics. These studies indicate the need for further improvement in land surface models.