Data from long‐term ecosystem monitoring and research stations in North America and results of simulations made with interpretive models indicate that changes in climate (precipitation and temperature) can have a significant effect on the quality of surface waters. Changes in water quality during storms, snowmelt, and periods of elevated air temperature or drought can cause conditions that exceed thresholds of ecosystem tolerance and, thus, lead to water‐quality degradation. If warming and changes in available moisture occur, water‐quality changes will likely first occur during episodes of climate‐induced stress, and in ecosystems where the factors controlling water quality are sensitive to climate variability. Continued climate stress would increase the frequency with which ecosystem thresholds are exceeded and thus lead to chronic water‐quality changes. Management strategies in a warmer climate will therefore be needed that are based on local ecological thresholds rather than annual median condition. Changes in land use alter biological, physical, and chemical processes in watersheds and thus significantly alter the quality of adjacent surface waters; these direct human‐caused changes complicate the interpretation of water‐quality changes resulting from changes in climate, and can be both mitigated and exacerbated by climate change. A rigorous strategy for integrated, long‐term monitoring of the ecological and human factors that control water quality is necessary to differentiate between actual and perceived climate effects, and to track the effectiveness of our environmental policies.

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Because reanalysis data sets offer state variables and fluxes at regular space / time intervals, atmospheric reanalyses have become a mainstay of the climate community for diagnostic purposes and for driving offline ocean and land models. Although one weakness of these data sets is the susceptibility of the flux products to uncertainties because of shortcomings in parameterized model physics, another issue, perhaps less appreciated, is the fact that continual but discreet changes in the evolving observational system, particularly from satellite sensors, may also introduce artifacts in the time series of quantities.In this paper we examine the ability of the NASA MERRA (Modern Era Retrospective Analysis for Research and Applications) and other recent reanalyses to determine variability in the climate system over the satellite record (~ the last 30 years). In particular we highlight the effect on the reanalysis of discontinuities at the junctures of the onset of passive microwave imaging (Special Sensor Microwave Imager) in late 1987 as well as improved sounding and imaging with the Advanced Microwave Sounding Unit, AMSU-A, in 1998. We first examine MERRA fluxes from the perspective of how physical modes of variability (e.g. ENSO events, Pacific Decadal Variability) are contamined by artificial step-like trends induced by the onset of new moisture data these two satellite observing systems. Secondly, we show how Redundancy Analysis, a statistical regression methodology, is effective in relating these artifact signals in the moisture and temperature analysis increments to their presence in the physical flux terms (e.g. precipitation, radiation). This procedure is shown to be effective greatly reducing the artificial trends in the flux quantities.https://ntrs.nasa.gov/search

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