Some of the latest papers on climate change impacts on hydrosphere are shown below. First a few highlighted papers with abstracts and then a list of some other papers. If this subject interests you, be sure to check also the other papers – they are by no means less interesting than the highlighted ones.
Ocean acidification over the next three centuries using a simple global climate carbon-cycle model: projections and sensitivities (Hartin et al. 2016) http://www.biogeosciences.net/13/4329/2016/
Abstract: Continued oceanic uptake of anthropogenic CO2 is projected to significantly alter the chemistry of the upper oceans over the next three centuries, with potentially serious consequences for marine ecosystems. Relatively few models have the capability to make projections of ocean acidification, limiting our ability to assess the impacts and probabilities of ocean changes. In this study we examine the ability of Hector v1.1, a reduced-form global model, to project changes in the upper ocean carbonate system over the next three centuries, and quantify the model’s sensitivity to parametric inputs. Hector is run under prescribed emission pathways from the Representative Concentration Pathways (RCPs) and compared to both observations and a suite of Coupled Model Intercomparison (CMIP5) model outputs. Current observations confirm that ocean acidification is already taking place, and CMIP5 models project significant changes occurring to 2300. Hector is consistent with the observational record within both the high- (> 55°) and low-latitude oceans (< 55°). The model projects low-latitude surface ocean pH to decrease from preindustrial levels of 8.17 to 7.77 in 2100, and to 7.50 in 2300; aragonite saturation levels (ΩAr) decrease from 4.1 units to 2.2 in 2100 and 1.4 in 2300 under RCP 8.5. These magnitudes and trends of ocean acidification within Hector are largely consistent with the CMIP5 model outputs, although we identify some small biases within Hector’s carbonate system. Of the parameters tested, changes in [H+] are most sensitive to parameters that directly affect atmospheric CO2 concentrations – Q10 (terrestrial respiration temperature response) as well as changes in ocean circulation, while changes in ΩAr saturation levels are sensitive to changes in ocean salinity and Q10. We conclude that Hector is a robust tool well suited for rapid ocean acidification projections and sensitivity analyses, and it is capable of emulating both current observations and large-scale climate models under multiple emission pathways.
Anthropogenic and climate-driven water depletion in Asia (Yi et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL069985/abstract
Abstract: Anthropogenic depletion of terrestrial water storage (TWS) can be alleviated in wet years and intensified in dry years, and this wet/dry pattern spanning seasons to years is termed climate variability. However, the anthropogenic and climate-driven changes have not been isolated in previous studies; thus, the estimated trend of changes in TWS is strongly dependent on the study period. Here we try to remove the influence of climate variability from the estimation of the anthropogenic contribution, which is an indicator of the environmental burden and important for TWS projections. Toward this end, we propose a linear relationship between the variation in water storage and precipitation. Factors related to the sensitivity of water storage to precipitation are given to correct for the climate variability, and the anthropogenic depletion of terrestrial water and groundwater in Asia is estimated to be −187 ± 38 Gt/yr and −100 ± 47 Gt/yr, respectively.
Are long tide gauge records in the wrong place to measure global mean sea level rise? (Thompson et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL070552/abstract
Abstract: Ocean dynamics, land motion, and changes in Earth’s gravitational and rotational fields cause local sea level change to deviate from the rate of global mean sea level rise. Here, we use observations and simulations of spatial structure in sea level change to estimate the likelihood that these processes cause sea level trends in the longest and highest-quality tide gauge records to be systematically biased relative to the true global mean rate. The analyzed records have an average 20th century rate of approximately 1.6 mm/yr, but based on the locations of these gauges, we show the simple average underestimates the 20th century global mean rate by 0.1 ± 0.2 mm/yr. Given the distribution of potential sampling biases, we find < 1% probability that observed trends from the longest and highest-quality TG records are consistent with global mean rates less than 1.4 mm/yr.
Development of a 0.5 deg global monthly raining day product from 1901-2010 (Stillman & Zeng, 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL070244/abstract
Abstract: While several long-term global datasets of monthly precipitation amount (P) are widely available, only the Climate Research Unit (CRU) provides long-term global monthly raining day number (N) data (i.e., daily precipitation frequency in a month), with P/N representing the daily precipitation intensity. However, because CRU N is based on a limited number of gauges, it is found to perform poorly over data sparse regions. By combining the CRU method with a short-term gauge-satellite merged global daily precipitation dataset (CMORPH) and a global long-term monthly precipitation dataset (GPCC) with far more gauges than used in CRU, a new 0.5 deg global N dataset from 1901-2010 is developed, which differs significantly from CRU N. Compared with three independent regional daily precipitation products over U.S., China, and South America based on much denser gauge networks than used in CRU, the new product shows significant improvement over CRU N.
Detection and delineation of glacial lakes and identification of potentially dangerous lakes of Dhauliganga basin in the Himalaya by remote sensing techniques (Jha & Khare, 2016) http://link.springer.com/article/10.1007%2Fs11069-016-2565-9
Abstract: Glaciers are retreating and thinning in the high altitude of the Himalayas due to global warming, causing into formation of numerous glacial lakes. It is necessary to monitor these glacial lakes consistently to save properties and lives downstream from probable disastrous glacial lake outburst flood. In this study, image processing software ArcGIS and ERDAS Imagine have been used to analyse multispectral image obtained by Earth resource satellite Landsat for delineating the glacial lakes with the help of image enhancement technique like NDWI. Landsat data since 1972 through 2013 have been used and maximum seven glacial lakes (L1–L7) have been detected and delineated in Dhauliganga catchment, they are situated above 4000 masl. The Glacial Lake L2 (Lat 30°26′45″E and Long 80°23′16″N) is the largest whose surface area was 132,300 m2 in Sept 2009, and L6 (Lat 30°23′27″E and Long 80°31′52″N) is highly unstable with variation rate −55 to +145 % with increasing trend. Additionally, glacial lakes L2 (Lat 30°26′45″E and Long 80°23′16″N) and L6 (Lat 30°23′27″E and Long 80°31′52″N) have been identified as potentially hazardous. These lakes may probably burst; as a result, huge reserve of water and debris may be released all on a sudden. This may transform into hazardous flash flood in downstream causing loss of lives, as well as the destruction of houses, bridges, fields, forests, hydropower stations, roads, etc. It is to note that Dhauliganga river considered in this study is a tributary of Kaliganga river, and should not be confused with its namesake the Dhauliganga river, which is a tributary of Alaknanda river.
Extreme hydrological changes in the southwestern US drive reductions in water supply to Southern California by mid century (Pagán et al. 2016) http://iopscience.iop.org/article/10.1088/1748-9326/11/9/094026/meta
Regionalizing Africa: Patterns of Precipitation Variability in Observations and Global Climate Models (Badr et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0182.1
Evidencing decadal and interdecadal hydroclimatic variability over the Central Andes (Segura et al. 2016) http://iopscience.iop.org/article/10.1088/1748-9326/11/9/094016/meta
The uncertainties and causes of the recent changes in global evapotranspiration from 1982 to 2010 (Dong & Dai, 2016) http://link.springer.com/article/10.1007%2Fs00382-016-3342-x
Spatial pattern of reference evapotranspiration change and its temporal evolution over Southwest China (Sun et al. 2016) http://rd.springer.com/article/10.1007%2Fs00704-016-1930-7
Climate change in the Blue Nile Basin Ethiopia: implications for water resources and sediment transport (Wagena et al. 2016) http://rd.springer.com/article/10.1007%2Fs10584-016-1785-z
Rainfall in Qatar: Is it changing? (Mamoon & Rahman, 2016) http://link.springer.com/article/10.1007%2Fs11069-016-2576-6
Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) (Liu et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-16-0023.1
A multi-satellite climatology of clouds, radiation and precipitation in southern West Africa and comparison to climate models (Hill et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016JD025246/abstract
Detection, Attribution and Projection of Regional Rainfall Changes on (Multi-) Decadal Time Scales: A Focus on Southeastern South America (Zhang et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0287.1
Which weather systems are projected to cause future changes in mean and extreme precipitation in CMIP5 simulations? (Utsumi et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016JD024939/abstract
Out-phased decadal precipitation regime shift in China and the United States (Yang & Fu, 2016) http://rd.springer.com/article/10.1007%2Fs00704-016-1907-6
Forcing of recent decadal variability in the Equatorial and North Indian Ocean (Thompson et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016JC012132/abstract
Proxy-based reconstruction of surface water acidification and carbonate saturation of the Levant Sea during the Anthropocene (Bialik & Sisma-Ventura, 2016) http://www.sciencedirect.com/science/article/pii/S2213305416300881
Understanding decreases in land relative humidity with global warming: conceptual model and GCM simulations (Byrne & O’Gorman, 2016) http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0351.1
Spatial trend analysis of Hawaiian rainfall from 1920 to 2012 (Frazier & Giambelluca, 2016) http://onlinelibrary.wiley.com/doi/10.1002/joc.4862/abstract
Mapping of West Siberian taiga wetland complexes using Landsat imagery: implications for methane emissions (Terentieva et al. 2016) http://www.biogeosciences.net/13/4615/2016/
Wind driven mixing at intermediate depths in an ice-free Arctic Ocean (Lincoln et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL070454/abstract
Seasonal Evolution of Supraglacial Lakes on an East Antarctic Outlet Glacier (Langley et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL069511/abstract
Temperature-salinity structure of the North Atlantic circulation and associated heat and freshwater transports (Xu et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-15-0798.1
Eustatic and Relative Sea Level Changes (Rovere et al. 2016) http://rd.springer.com/article/10.1007%2Fs40641-016-0045-7
A mechanism for the response of the zonally asymmetric subtropical hydrologic cycle to global warming (Levine & Boos, 2016) http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-15-0826.1
Quantifying the contribution of glacier-melt water in the expansion of the largest lake in Tibet (Tong et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016JD025424/abstract