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- Author or Editor: Kazuhiro Oshima x
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Abstract
Recent years have seen an obvious warming trend in the Arctic. Streamflow and water temperature T w are important parameters representing the changes of Arctic rivers under climate change. However, few quantitative assessments of changes in river T w have been conducted at the pan-Arctic scale. To carry out such an assessment, this study used a modeling framework combining a land process model [the coupled hydrological and biogeochemical model (CHANGE)] with models of river discharge Q, ice cover, and T w dynamics. The T w model was improved by incorporating heat exchange at the air–water interface and heat advection from upstream through the channel network. The model was applied to pan-Arctic terrestrial rivers flowing into the Arctic Ocean over the period 1979–2013 and quantitatively assessed trends of T w at regional and pan-Arctic scales. The simulated T w values were consistent with observations at the mouths of major pan-Arctic rivers. The model simulations indicated a warming trend of T w by 0.16°C decade−1 at the outlets of the pan-Arctic rivers, including widespread spatial warming consistent with increased air temperature T a . The strong impact of T a on T w was verified by model sensitivity analysis based on various scenarios involving changes in the T a and Q forcings. Finally, this study demonstrated the warming of T w in Arctic rivers induced by T a warming, suggesting the potential for warming T w of Arctic rivers under future climate change scenarios.
Abstract
Recent years have seen an obvious warming trend in the Arctic. Streamflow and water temperature T w are important parameters representing the changes of Arctic rivers under climate change. However, few quantitative assessments of changes in river T w have been conducted at the pan-Arctic scale. To carry out such an assessment, this study used a modeling framework combining a land process model [the coupled hydrological and biogeochemical model (CHANGE)] with models of river discharge Q, ice cover, and T w dynamics. The T w model was improved by incorporating heat exchange at the air–water interface and heat advection from upstream through the channel network. The model was applied to pan-Arctic terrestrial rivers flowing into the Arctic Ocean over the period 1979–2013 and quantitatively assessed trends of T w at regional and pan-Arctic scales. The simulated T w values were consistent with observations at the mouths of major pan-Arctic rivers. The model simulations indicated a warming trend of T w by 0.16°C decade−1 at the outlets of the pan-Arctic rivers, including widespread spatial warming consistent with increased air temperature T a . The strong impact of T a on T w was verified by model sensitivity analysis based on various scenarios involving changes in the T a and Q forcings. Finally, this study demonstrated the warming of T w in Arctic rivers induced by T a warming, suggesting the potential for warming T w of Arctic rivers under future climate change scenarios.
Abstract
The summer northern annular mode (NAM) and the winter North Atlantic Oscillation (NAO)/winter NAM have a positive correlation from the mid-1960s to the 1980s. Namely, when the winter NAO/NAM is in a positive phase, the following summer NAM tended to be in a positive phase. During the period from the mid-1960s to the 1980s, the NAO/NAM signals extended to the stratosphere in winter. Also, the lower-tropospheric warm anomaly over northern Eurasia in winter associated with the positive phase of NAO/NAM continued into spring. In summer, the annular anomalies in the temperature and 500-hPa height fields appeared, and the high-latitude westerly wind was enhanced following the winter positive NAO/NAM. However, after circa 1990, the seasonal linkage was broken (i.e., the winter-to-summer correlation became insignificant). The stratospheric signal in the winter NAO/NAM became weak and summer signals associated with the winter NAO/NAM almost disappeared. Seasonal evolutions of atmospheric circulation and sea surface temperature (SST) anomalies associated with the winter NAO are examined for an early good-linkage period and a recent poor-linkage period. We discuss the possible causes of the linkage breakdown such as stratospheric ozone, North Atlantic SST, and Atlantic multidecadal oscillation, besides chaotic internal variability in the climate system. Simulations with the Community Earth System Model suggest that the ocean and/or sea ice with interseasonal memories possibly cause the linkage, besides large internal variability through which the linkage can take place by chance.
Abstract
The summer northern annular mode (NAM) and the winter North Atlantic Oscillation (NAO)/winter NAM have a positive correlation from the mid-1960s to the 1980s. Namely, when the winter NAO/NAM is in a positive phase, the following summer NAM tended to be in a positive phase. During the period from the mid-1960s to the 1980s, the NAO/NAM signals extended to the stratosphere in winter. Also, the lower-tropospheric warm anomaly over northern Eurasia in winter associated with the positive phase of NAO/NAM continued into spring. In summer, the annular anomalies in the temperature and 500-hPa height fields appeared, and the high-latitude westerly wind was enhanced following the winter positive NAO/NAM. However, after circa 1990, the seasonal linkage was broken (i.e., the winter-to-summer correlation became insignificant). The stratospheric signal in the winter NAO/NAM became weak and summer signals associated with the winter NAO/NAM almost disappeared. Seasonal evolutions of atmospheric circulation and sea surface temperature (SST) anomalies associated with the winter NAO are examined for an early good-linkage period and a recent poor-linkage period. We discuss the possible causes of the linkage breakdown such as stratospheric ozone, North Atlantic SST, and Atlantic multidecadal oscillation, besides chaotic internal variability in the climate system. Simulations with the Community Earth System Model suggest that the ocean and/or sea ice with interseasonal memories possibly cause the linkage, besides large internal variability through which the linkage can take place by chance.
Abstract
A land process model [the coupled hydrological and biogeochemical model (CHANGE)] is used to quantitatively assess changes in the ice phenology, thickness, and volume of terrestrial Arctic rivers from 1979 to 2009. The CHANGE model was coupled with a river routing and discharge model enabling explicit representation of river ice and water temperature dynamics. Model-simulated river ice phenological dates and thickness were generally consistent with in situ river ice data and landscape freeze–thaw (FT) satellite observations. Climate data indicated an increasing trend in winter surface air temperature (SAT) over the pan-Arctic during the study period. Nevertheless, the river ice thickness simulations exhibited a thickening regional trend independent of SAT warming, and associated with less insulation and cooling of underlying river ice by thinning snow cover. Deeper snow depth (SND) combined with SAT warming decreased simulated ice thickness, especially for Siberian rivers, where ice thickness is more strongly correlated with SND than SAT. Overall, the Arctic river ice simulations indicated regional trends toward later fall freezeup, earlier spring breakup, and consequently a longer annual ice-free period. The simulated ice phenological dates were significantly correlated with seasonal SAT warming. It is found that SND is an important factor for winter river ice growth, while ice phenological timing is dominated by seasonal SAT. The mean total Arctic river ice volume simulated from CHANGE was 54.1 km3 based on the annual maximum ice thickness in individual grid cells, while river ice volume for the pan-Arctic rivers decreased by 2.82 km3 (0.5%) over the 1979–2009 record. Arctic river ice is shrinking as a consequence of regional climate warming and coincident with other cryospheric components, including permafrost, glaciers, and sea ice.
Abstract
A land process model [the coupled hydrological and biogeochemical model (CHANGE)] is used to quantitatively assess changes in the ice phenology, thickness, and volume of terrestrial Arctic rivers from 1979 to 2009. The CHANGE model was coupled with a river routing and discharge model enabling explicit representation of river ice and water temperature dynamics. Model-simulated river ice phenological dates and thickness were generally consistent with in situ river ice data and landscape freeze–thaw (FT) satellite observations. Climate data indicated an increasing trend in winter surface air temperature (SAT) over the pan-Arctic during the study period. Nevertheless, the river ice thickness simulations exhibited a thickening regional trend independent of SAT warming, and associated with less insulation and cooling of underlying river ice by thinning snow cover. Deeper snow depth (SND) combined with SAT warming decreased simulated ice thickness, especially for Siberian rivers, where ice thickness is more strongly correlated with SND than SAT. Overall, the Arctic river ice simulations indicated regional trends toward later fall freezeup, earlier spring breakup, and consequently a longer annual ice-free period. The simulated ice phenological dates were significantly correlated with seasonal SAT warming. It is found that SND is an important factor for winter river ice growth, while ice phenological timing is dominated by seasonal SAT. The mean total Arctic river ice volume simulated from CHANGE was 54.1 km3 based on the annual maximum ice thickness in individual grid cells, while river ice volume for the pan-Arctic rivers decreased by 2.82 km3 (0.5%) over the 1979–2009 record. Arctic river ice is shrinking as a consequence of regional climate warming and coincident with other cryospheric components, including permafrost, glaciers, and sea ice.
Abstract
The Arctic Ocean is known to be quiescent in terms of turbulent kinetic energy (TKE) associated with internal waves. To investigate the current state of TKE in the seasonally ice-free Chukchi Plateau, Arctic Ocean, this study performed a 3-week, fixed-point observation (FPO) using repeated microstructure, hydrographic, and current measurements in September 2014. During the FPO program, the microstructure observation detected noticeable peaks of TKE dissipation rate ε during the transect of an anticyclonic eddy moving across the FPO station. Particularly, ε had a significant elevation in the lower halocline layer, near the critical level, reaching the order of 10−8 W kg−1. The ADCP-measured current displayed energetic near-inertial internal waves (NIWs) propagating via the stratification at the top and bottom of the anticyclone. According to spectral analyses of horizontal velocity, the waves had almost downward energy propagation, and its current amplitude reached ~10 cm s−1. The WKB scaling, incorporating vertical variations of relative vorticity, suggests that increased wave energy near the two pycnoclines was associated with diminishing group velocity at the corresponding depths. The finescale parameterization using observed near-inertial velocity and buoyancy frequency successfully reproduced the characteristics of observed ε, supporting that the near-inertial kinetic energy can be effectively dissipated into turbulence near the critical layer. According to a mixed layer slab model, a rapidly moving storm that has passed over in the first week likely delivered the bulk of NIW kinetic energy, eventually captured by the vortex, into the surface water.
Abstract
The Arctic Ocean is known to be quiescent in terms of turbulent kinetic energy (TKE) associated with internal waves. To investigate the current state of TKE in the seasonally ice-free Chukchi Plateau, Arctic Ocean, this study performed a 3-week, fixed-point observation (FPO) using repeated microstructure, hydrographic, and current measurements in September 2014. During the FPO program, the microstructure observation detected noticeable peaks of TKE dissipation rate ε during the transect of an anticyclonic eddy moving across the FPO station. Particularly, ε had a significant elevation in the lower halocline layer, near the critical level, reaching the order of 10−8 W kg−1. The ADCP-measured current displayed energetic near-inertial internal waves (NIWs) propagating via the stratification at the top and bottom of the anticyclone. According to spectral analyses of horizontal velocity, the waves had almost downward energy propagation, and its current amplitude reached ~10 cm s−1. The WKB scaling, incorporating vertical variations of relative vorticity, suggests that increased wave energy near the two pycnoclines was associated with diminishing group velocity at the corresponding depths. The finescale parameterization using observed near-inertial velocity and buoyancy frequency successfully reproduced the characteristics of observed ε, supporting that the near-inertial kinetic energy can be effectively dissipated into turbulence near the critical layer. According to a mixed layer slab model, a rapidly moving storm that has passed over in the first week likely delivered the bulk of NIW kinetic energy, eventually captured by the vortex, into the surface water.
Abstract
The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.
Abstract
The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.
