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By combining the simulations of more than a dozen different global land surface models, an unprecedented analysis of terrestrial water and energy budgets is realized.

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This study quantifies mean annual and monthly fluxes of Earth's water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first and data-integrating models second. A careful accounting of uncertainty in the estimates is included. It is applied within a routine that enforces multiple water and energy budget constraints simultaneously in a variational framework in order to produce objectively determined optimized flux estimates. In the majority of cases, the observed annual surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are negligible. Fluxes were poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian islands, leading to reliance on atmospheric analysis estimates. Many of the satellite systems that contributed data have been or will soon be lost or replaced. Models that integrate ground-based and remote observations will be critical for ameliorating gaps and discontinuities in the data records caused by these transitions. Continued development of such models is essential for maximizing the value of the observations. Next-generation observing systems are the best hope for significantly improving global water budget accounting.

New objectively balanced observation-based reconstructions of global and continental energy budgets and their seasonal variability are presented that span the golden decade of Earth-observing satellites at the start of the twentyfirst century. In the absence of balance constraints, various combinations of modern flux datasets reveal that current estimates of net radiation into Earth's surface exceed corresponding turbulent heat fluxes by 13-24 W m 22. The largest imbalances occur over oceanic regions where the component algorithms operate independent of closure constraints. Recent uncertainty assessments suggest that these imbalances fall within anticipated error bounds for each dataset, but the systematic nature of required adjustments across different regions confirm the existence of biases in the component fluxes. To reintroduce energy and water cycle closure information lost in the development of independent flux datasets, a variational method is introduced that explicitly accounts for the relative accuracies in all component fluxes. Applying the technique to a 10-yr record of satellite observations yields new energy budget estimates that simultaneously satisfy all energy and water cycle balance constraints. , consistent with recent observations of changes in ocean heat content. Annual mean energy budgets and their seasonal cycles for each of seven continents and nine ocean basins are also presented.

The characteristics of eight global soil wetness products, three produced by land surface model calculations, three from coupled land-atmosphere model reanalyses, and two from microwave remote sensing estimates, have been examined. The goal of this study is to determine whether there exists an optimal dataset for the initialization of the land surface component of global weather and climate forecast models. Their abilities to simulate the phasing of the annual cycle and to accurately represent interannual variability in soil wetness by comparing to available in situ measurements are validated. Because soil wetness climatologies vary greatly among land surface models, and models have different operating ranges for soil wetness (i.e., very different mean values, variances, and hydrologically critical thresholds such as the point where evaporation occurs at the potential rate or where surface runoff begins), one cannot simply take the soil wetness field from one product and apply it to an arbitrary land surface scheme (LSS) as an initial condition without experiencing some sort of initialization shock. A means of renormalizing soil wetness is proposed based on the local statistical properties of this field in the source and target models, to allow a large number of climate models to apply the same initialization in multimodel studies or intercomparisons. As a test of feasibility, renormalization among the model-derived products is applied to see how it alters the character of the soil wetness climatologies.

We assess the ability of global water systems, resolved at 282 assessment subregions (ASRs), to the meet water requirements under integrated projections of socioeconomic growth and climate change. We employ a water resource system (WRS) component embedded within the Massachusetts Institute of Technology Integrated Global System Model (IGSM) framework in a suite of simulations that consider a range of climate policies and regional hydroclimate changes out to 2050. For many developing nations, water demand increases due to population growth and economic activity have a much stronger effect on water stress than climate change. By 2050, economic growth and population change alone can lead to an additional 1.8 billion people living under at least moderate water stress, with 80% of these located in developing countries. Uncertain regional climate change can play a secondary role to either exacerbate or dampen the increase in water stress. The strongest climate impacts on water stress are observed in Africa, but strong impacts also occur over Europe, Southeast Asia, and North America. The combined effects of socioeconomic growth and uncertain climate change lead to a 1.0-1.3 billion increase of the world's 2050 projected population living with overly exploited water conditions-where total potential water requirements will consistently exceed surface water supply. This would imply that adaptive measures would be taken to meet these surface water shortfalls and include: water-use efficiency, reduced and/or redirected consumption, recurrent periods of water emergencies or curtailments, groundwater depletion, additional interbasin transfers, and overdraw from flow intended to maintain environmental requirements.

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.