Quaternary Caspian Sea level variations depended on geophysical processes (affecting the opening and closing of gateways and basin size/shape) and hydro-climatological processes (affecting water balance). Disentangling the drivers of past Caspian Sea level variation, as well as the mechanisms by which they impacted the Caspian Sea level variation, is much debated. In this study we examine the relative impacts of hydroclimatic change, ice-sheet accumulation and melt, and isostatic adjustment on Caspian Sea level change. We performed model analysis of ice-sheet and hydroclimate impacts on Caspian Sea level and compared these with newly collated published palaeo-Caspian sea level data for the last glacial cycle. We used palaeoclimate model simulations from a global coupled ocean-atmosphere-vegetation climate model, HadCM3, and ice-sheet data from the ICE-6G_C glacial isostatic adjustment model. Our results show that ice-sheet meltwater during the last glacial cycle played a vital role in Caspian Sea level variations, which is in agreement with hypotheses based on palaeo-Caspian Sea level information. The effect was directly linked to the reorganization and expansion of the Caspian Sea palaeo-drainage system resulting from topographic change. The combined contributions from meltwater and runoff from the expanded basin area were primary factors in the Caspian Sea transgression during the deglaciation period between 20 and 15 kyr BP. Their impact on the evolution of Caspian Sea level lasted until around 13 kyr BP. Millennial scale events (Heinrich events and the Younger Dryas) negatively impacted the surface water budget of the Caspian Sea but their influence on Caspian Sea level variation was short-lived and was outweighed by the massive combined meltwater and runoff contribution over the expanded basin.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.