Impact of vegetation feedback on the temperature and its diurnal range over the Northern Hemisphere during summer in a 2 × CO2 climate

This study examines the potential impact of vegetation feedback on the changes in the diurnal temperature range (DTR) due to the doubling of atmospheric CO₂ concentrations during summer over the Northern Hemisphere using a global climate model equipped with a dynamic vegetation model. Results show that CO₂ doubling induces significant increases in the daily mean temperature and decreases in DTR regardless of the presence of the vegetation feedback effect. In the presence of vegetation feedback, increase in vegetation productivity related to warm and humid climate lead to (1) an increase in vegetation greenness in the mid-latitude and (2) a greening and the expansion of grasslands and boreal forests into the tundra region in the high latitudes. The greening via vegetation feedback induces contrasting effects on the temperature fields between the mid- and high-latitude regions. In the mid-latitudes, the greening further limits the increase in T _max more than T _min, resulting in further decreases in DTR because the greening amplifies evapotranspiration and thus cools daytime temperature. The greening in high-latitudes, however, it reinforces the warming by increasing T _max more than T _min to result in a further increase in DTR from the values obtained without vegetation feedback. This effect on T _max and DTR in the high latitude is mainly attributed to the reduction in surface albedo and the subsequent increase in the absorbed insolation. Present study indicates that vegetation feedback can alter the response of the temperature field to increases in CO₂ mainly by affecting the T _max and that its effect varies with the regional climate characteristics as a function of latitudes.     

Temperature trends in the skin/surface, mid-troposphere and low stratosphere near Korea from satellite and ground measurements

Various types of satellite (AIRS/AMSU, MODIS) and ground measurements are used to analyze temperature trends in the four vertical layers (skin/surface, mid-troposphere, and low stratosphere) around the Korean Peninsula (123–132°E, 33–44°N) during the period from September 2002 to August 2010. The ground-based observations include 72 Surface Meteorological Stations (SMSs), 6 radiosonde stations (RAOBs), 457 Automatic Weather Stations (AWSs) over the land, and 5 buoy stations over the ocean. A strong warming (0.052 K yr^?1) at the surface, and a weak warming (0.004～0.010 K yr^?1) in the mid-troposphere and low stratosphere have been found from satellite data, leading to an unstable atmospheric layer. The AIRS/AMSU warming trend over the ocean surface around the Korean Peninsula is about 2.5 times greater than that over the land surface. The ground measurements from both SMS and AWS over the land surface of South Korea also show a warming of 0.043～0.082 K yr^?1, consistent with the satellite observations. The correlation average (r = 0.80) between MODIS skin temperature and ground measurement is significant. The correlations between AMSU and RAOB are very high (0.91～0.95) in the anomaly time series, calculated from the spatial averages of monthly mean temperature values. However, the warming found in the AMSU data is stronger than that from the RAOB at the surface. The opposite feature is present above the mid-troposphere, indicating that there is a systematic difference. Warming phenomena (0.012～0.078 K yr^?1) are observed from all three data sets (SMS, AWS, MODIS), which have been corroborated by the coincident measurements at five ground stations. However, it should also be noted that the observed trends are subject to large uncertainty as the corresponding 95% confidence intervals tend to be larger than the observed signals due to large thermal variability and the relatively short periods of the satellitebased temperature records. The EOF analysis of monthly mean temperature anomalies indicates that the tropospheric temperature variability near Korea is primarily linked to the Arctic Oscillation (AO), and secondarily to ENSO (El Niño and Southern Oscillation). However, the low stratospheric temperature variability is mainly associated with Southern Oscillation and then additionally with Quasi-Biennial Oscillation (QBO). Uncertainties from the different spatial resolutions between satellite data are discussed in the trends.     

A dissection of the surface temperature biases in the Community Earth System Model

Based upon the climate feedback-responses analysis method, a quantitative attribution analysis is conducted for the annual-mean surface temperature biases in the Community Earth System Model version 1 (CESM1). Surface temperature biases are decomposed into partial temperature biases associated with model biases in albedo, water vapor, cloud, sensible/latent heat flux, surface dynamics, and atmospheric dynamics. A globally-averaged cold bias of ?1.22 K in CESM1 is largely attributable to albedo bias that accounts for approximately ?0.80 K. Over land, albedo bias contributes ?1.20 K to the averaged cold bias of ?1.45 K. The cold bias over ocean, on the other hand, results from multiple factors including albedo, cloud, oceanic dynamics, and atmospheric dynamics. Bias in the model representation of oceanic dynamics is the primary cause of cold (warm) biases in the Northern (Southern) Hemisphere oceans while surface latent heat flux over oceans always acts to compensate for the overall temperature biases. Albedo bias resulted from the model’s simulation of snow cover and sea ice is the main contributor to temperature biases over high-latitude lands and the Arctic and Antarctic region. Longwave effect of water vapor is responsible for an overall warm (cold) bias in the subtropics (tropics) due to an overestimate (underestimate) of specific humidity in the region. Cloud forcing of temperature biases exhibits large regional variations and the model bias in the simulated ocean mixed layer depth is a key contributor to the partial sea surface temperature biases associated with oceanic dynamics. On a global scale, biases in the model representation of radiative processes account more for surface temperature biases compared to non-radiative, dynamical processes.     

A copula-based multivariate analysis of Canadian RCM projected changes to flood characteristics for northeastern Canada

In the present work, climate change impacts on three spring (March–June) flood characteristics, i.e. peak, volume and duration, for 21 northeast Canadian basins are evaluated, based on Canadian regional climate model (CRCM) simulations. Conventional univariate frequency analysis for each flood characteristic and copula based bivariate frequency analysis for mutually correlated pairs of flood characteristics (i.e. peak–volume, peak–duration and volume–duration) are carried out. While univariate analysis is focused on return levels of selected return periods (5-, 20- and 50-year), the bivariate analysis is focused on the joint occurrence probabilities P1 and P2 of the three pairs of flood characteristics, where P1 is the probability of any one characteristic in a pair exceeding its threshold and P2 is the probability of both characteristics in a pair exceeding their respective thresholds at the same time. The performance of CRCM is assessed by comparing ERA40 (the European Centre for Medium-Range Weather Forecasts 40-year reanalysis) driven CRCM simulated flood statistics and univariate and bivariate frequency analysis results for the current 1970–1999 period with those observed at selected 16 gauging stations for the same time period. The Generalized Extreme Value distribution is selected as the marginal distribution for flood characteristics and the Clayton copula for developing bivariate distribution functions. The CRCM performs well in simulating mean, standard deviation, and 5-, 20- and 50-year return levels of flood characteristics. The joint occurrence probabilities are also simulated well by the CRCM. A five-member ensemble of the CRCM simulated streamflow for the current (1970–1999) and future (2041–2070) periods, driven by five different members of a Canadian Global Climate Model ensemble, are used in the assessment of projected changes, where future simulations correspond to A2 scenario. The results of projected changes, in general, indicate increases in the marginal values, i.e. return levels of flood characteristics, and the joint occurrence probabilities P1 and P2. It is found that the future marginal values of flood characteristics and P1 and P2 values corresponding to longer return periods will be affected more by anthropogenic climate change than those corresponding to shorter return periods but the former ones are subjected to higher uncertainties.     

Wave Climatology in Coastal Maine for Aquaculture and Other Applications

Wind waves represent a significant hydrodynamic factor affecting many oceanographic studies such as sediment transport, design of structures, etc. In coastal Maine, wave information is needed, among other applications, for aquaculture-related activities. As few data sources exist, a question that confronts scientists pertains to the magnitudes of typical and extreme wave conditions at various times. To address this, numerical modeling was performed for a period of six and a half years (7/99–12/05) on a continuous basis by coupling National Oceanic and Atmospheric Administration’s outer ocean predictions to two coastal, high-resolution, regional domain grids encompassing the Penobscot Bay and Machias Bay regions where aquaculture activity is prevalent and expanding. As the modeling involves uncertainties because of bathymetric and wind field representations, their effect on the results was explored. It was found that although the uncertainties could create inaccuracies in real-time forecasts, their effect on the development of climatogies was minimal. Average modeled significant wave heights are found to vary between 0.6 and 1.5 m in the sub-domains. The maximum conditions are of the order of 6.5 m in the outer parts of the sub-domains and occurred in September and December. Estimated wave-induced bottom velocities were found in many areas to be in excess of the published estimates of resuspension thresholds for net-pen wastes. Estimates of “extreme” wave conditions, corresponding to a recurrence interval of 30 years (representing the nominal design life of the cage), were found to vary between 2 and 7 m in the modeled areas. Detailed contour maps have been developed for site-specific characterization of the wave climate.     

Prediction of nonstationary climatic series

Summary Most of the stochastic prediction methods are developed for stationary time series. However, many climatic series show clear evidence of non-stationarity. In such cases, methods based on the stationarity assumptions would be inappropriate. Alternative methods such as those based on stochastic approximation are preferable in these cases because they are based on adaptive learning principles. These methods have not been applied and their suitability not tested with nonstationary climatic time series.In the stochastic approximation method, the deterministic component of a nonstationary time series is estimated by first predicting the two steps ahead value of a time series. The two steps-ahead forecast may involve a term characterizing the trend in the time series. The two steps-ahead predictor is corrected to obtain the one step ahead prediction by using a gain sequence.The dynamic stochastic approximation method is used herein to predict non-stationary climatic time series. Daily minimum temperature series at West Lafayette, Indiana, U.S.A. and seasonal temperature and precipitation series at Evansville, Indiana, U.S.A. are used in the study. For data trends, an improved dynamic stochastic approximation method, called the modified dynamic stochastic approximation method gives more accurate predictions. If the method is used for seasonal data, then it can be used to track the time varying mean value.With 6 Figures     

Role of Siliceous Fossil Dissolution in Downcore Variations of Grain Size and Water Content: Western Margin of Clarion-Clipperton Fracture Zone, NE Equatorial Pacific

Grain size and water content in box-core sediments from the Clarion-Clipperton fracture zone (C-C zone) in the northeast equatorial Pacific were analyzed in detail to understand the downcore variations across a hiatus between Quaternary and Tertiary layers. Grain-size distributions in the topmost core sediments show two modes: a coarse mode (peaked at 50 μm) and a fine mode (at 2-25 μm). The coarse mode disappears gradually with depth accompanied by the dissolution of siliceous fossil tests, whereas the fine mode coarsens due to the formation of authigenic minerals. Water content increases abruptly across a color boundary between an upper pale brown layer and a lower dark brown layer that is the hiatus between Quaternary and Tertiary layers. Abundant smectites and microvoid molds, which are created by the prolonged fossil dissolution in the underlying sediment, are attributed for the abrupt downcore variation of water content. Overall variations in grain size and water content in the topmost core sediments in the western C-C zone are possibly constrained by the dissolution of biogenic siliceous fossils. Variations in geotechnical properties related to these changes must be considered in the design of nodule collectors.     

Geomechanical modeling of hydrate‐bearing sediments during dissociation under shear

Methane hydrate‐bearing sediments exist throughout the world in continental margins and in Arctic permafrost. Hydrates are ice‐like compounds when dissociate due to temperature rise or reduction in fluid pressure, release gas. Because of the mechanical property changes caused by dissociation in which the loads supported by the hydrates are transferred to soil grains, these sediments may become unstable. To quantify the risk of ground instability triggered by dissociation, which may happen during operation to extract methane gas or from climate changes, a reliable predictive model is indispensable. Even though many models have been proposed, a detailed validation of the ability to model dissociation impact is still needed. This study investigated the adequacy of an spatially mobilized plane constitutive model and a modeling framework using laboratory‐induced dissociation tests under shear from literature. Using laboratory‐imposed temperature and pressure changes and the resulting hydrate saturation changes as input, this study was able to capture the geomechanical responses and determine the stability state of methane hydrate‐bearing sediments as observed. Copyright © 2017 John Wiley & Sons, Ltd.