T. putitora, inhabiting the mountain rivers, is a game and food fish with long life span. The Himalayan mahseer exhibited slow growth comparatively to its first cousin T. tor that is habiting in tropical waters. Analysis of the decadal trends revealed that asymptotic length (L ∞ ) decreased from
permanent high pressure cells (centred around 30 ◦ S), and finally for the mid-latitude southern part, by cyclones and anticyclones associated with the polar front in a generally westerly air flow (e.g. Fonseca de Albuquerque et al., 2009). Temperature trends over the last few decades estimated, for example, from the CRU climatology (Mitchell and Jones, 2005) reveal a warming trend for the Amazon Basin and Brazil, and constant temperatures or even a slight cool- ing of the continent to the south of Brazil and in the north- west of the continent (Colombia). Regarding precipitation, sufficiently long records for the purpose of robust trend anal- ysis exist, but unfortunately, with few exceptions, these are only available for outside the Amazon Basin (e.g. Haylock et al., 2006). The pattern revealed by these data is, how- ever, a positive trend in the region from approximately 20 ◦ S down to Argentina and stretching from the eastern foothills of the Andes to the Atlantic coast. The second pattern is a decreasing trend in a stretch along the Pacific coast and up along the western flank of the Andes (CRU climatology; Mitchell and Jones, 2005; Haylock et al., 2006). The already mentioned increasing precipitation trend from approximately 20 ◦ S southwards is mirrored by a strongly increasing trend of the La Plata River discharge into the Atlantic at Buenos Aires (e.g. Milly et al., 2005 and references therein). These positive trends are very likely the result of an increasing wa- ter vapour outflow from the Amazon Basin towards the south (Rao et al., 1996).
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Abstract. Sandy shorelines are dynamic with constant changes that can cause hazards in developed areas. The causes of change may be either natural or anthropogenic. This paper evaluates evidence for shoreline changes and their causative factors using a case study on the east coast of South Africa. Beach morphology trends were found to be location- specific, but overall the beaches show a receding trend. It was hypothesized that wave, tide, sea level and wind trends as well as anthropogenic influences are causative factors, and their contributions to shoreline changes were evaluated. Maximum significant wave heights, average wave direction, peak period and storm event frequencies all show weak in- creasing trends, but only the increases in peak period and wave direction are statistically significant. The chronic beach erosion cannot be attributed to wave climate changes since they are still too small to explain the observations. Instead, the impacts of sea level rise and reductions in the supply of beach sediments are suggested as the main causative factors. The analysis also identifies a trend in the frequency of severe erosion events due to storms that coincide with a 4.5-yr ex- treme tide cycle, which demonstrates the potential impact of future sea level rise.
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Global surface temperatures are one of the parameters most commonly used to discuss the evolution of climate. Databases of instrumental temperatures that cover the past century and a half have been compiled by four main groups (Climate Research Unit at the University of East Anglia—CRU, NASA Goddard Institute for Space Stud- ies—GISS, National Oceanic and Atmospheric Adminis- tration—NOAA, Berkeley Earth Surface Temperature consortium—BEST). This is a huge, difficult task and these databases are necessarily faced with a number of li- mitations: the geographical distribution of stations is far from uniform and varies with time; also, there may be fundamental difficulties in establishing meaningful glo- bal temperatures for the Earth . Climate is generally defined as the ~30 year average of weather and since only 100 to 150 years of instrumental data are available, this places severe limitations on the significance of glo- bal temperature trends and multi-decadal oscillations. A monotonic (low degree) trend can be fit to all global tem-
Brohan et al., 2006). This has the advantage to introduce lim- ited perturbations in the model dynamics (e.g. Dubinkina et al., 2011; Dubinkina and Goosse, 2013) and to provide esti- mates over the 20th century that are as homogenous as pos- sible (Goosse et al., 2009). In comparison, the atmospheric reanalyses often have shifts in the late 1970s associated with the introduction of satellite measurements in the set of assim- ilated data, which leads to spurious long-term trends (Kistler et al., 2001; Fichefet et al., 2003; Marshall, 2003). Never- theless, because of this weak constraint and because of the low resolution of LOVECLIM, we do not expect to simulate the details of the observed trends. Even sea ice–ocean mod- els driven by the best estimates of atmospheric forcing have trouble in reproducing the sea ice variability in the Southern Ocean over the last decades (Fichefet et al., 2003; Zhang, 2007; Massonnet et al., 2011). We are, however, able to sim- ulate a large decrease of the ice concentration in the Belling- shausen Sea and in the western Weddell Sea and an increase in the majority of the other sectors in good agreement with observations (Fig. 10a). The overall trend in ice extent is of − 38 ± 93 × 10 3 km 2 per decade over the period 1979–2009. This implies that the ensemble mean of the simulation with data assimilation underestimates the observed increase. The
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In this paper, we describe a stochastic model of salmon parr recruitment, growth, survival, and smolting that we have applied to four decades of data from a Scottish upland stream (the Girnock Burn). From this we draw two principal conclusions: first, that trends in age at smolting are compat- ible with causation by a temperature-dependent growth rate in the form of an activated process with an activation tem- perature close to that given for Salmo trutta by Elliott (1976); and second, that the remarkably small change in to- tal smolt output, in the context of a 70% decrease in spawner input, must be ascribed to strong density depend- ence in ova or fry survival — caricatured in this model by a spawner–fry stock–recruitment relation. This relation ap- pears to have been altered by the ova-planting programme affecting cohorts hatched from 2001 onwards.
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Similar to global land temperature trends, the Amazon region has warmed by approximately 0.5–0.6 ◦ C over the last few decades (1960 to 2000, e.g. Victoria et al., 1998; Malhi and Wright, 2004). Published analyses of precipita- tion trends by various authors differ in the periods chosen, and climatologies or station data used (Espinoza et al., 2009). This is partially due to the sparsity of precipitation records in the Amazon already noted. Nevertheless, river discharge data should also provide a good diagnostic of hydrologi- cal cycle changes with the rate of discharge to the ocean providing a measure of the Basin-wide precipitation in ex- cess of plant requirements, and the following patterns emerge when analysing trends in Amazon river discharge at Obidos (Call`ede et al., 2004; Fig. 4), located approximately 800 km inland from the estuary of the Amazon River. At this point the River drains a basin of ∼ 4.7 × 10 6 km 2 , or roughly 77 % of the Amazon Basin proper. Although such data suffer from a shortcoming that the measured discharge is “blind” to whether water falling as precipitation has been recirculated via transpiration or not, as is shown in Fig. 4, the last ∼ 100 yr exhibit a substantial increasing trend (approximately 20 % change from 1900 to 2010), arguing for a similar trend in annual mean net precipitation. A second noteworthy feature which can be inferred from Fig. 4 is that wet seasons have become more pronounced and inter-annual variation has in- creased over the last decades.
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This exercise produced an almost continuous annual time series for EC since 1963, and WL since about 1950, but not all years were considered to be sufficiently representative for use in constructing the climatic model. The composite time series contained only years with a reasonably comprehensive and consistent coverage of the state, and at least 100 mea- surements in the case of EC, and 1000 in the case of ground- water. Figure 3 summarises the distribution of EC measure- ment sites in Queensland over the period of record. The rep- resentative period for modelling was taken as 1968–2002 for EC, and 1968–2001 for WL. It is accepted that the distribu- tion of gauging stations shown on Fig. 3 indicates that the EC time series would be most representative of the coastal and central regions. However, the similarity of the pattern with that observed throughout the state in QDPI (1994) indicates that it is general enough for a preliminary study aimed at ver- ifying a climatic influence on the decadal trends observed. The WL pattern resembled plots prepared by Jolly and Chin (1991) for bores in undisturbed areas of the Northern Terri- tory to the west of Queensland which have been monitored since the 1950s.
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Some implications can be derived from these conclusions. Although the future anthropogenic sea-level trends in the fu- ture in the North Atlantic and probably area-averaged sea level will roughly follow the global sea-level rise caused by anthropogenic warming, the decadal variations around this trend linked to natural climate variations may be different in the North Atlantic and within the Baltic Sea itself. The effects of the trends in the SLP are spatially heterogeneous and most important in wintertime. The effect of precipitation trends, assuming that the same mechanisms remain unaltered in the future, may affect some seasons more strongly than others, and therefore the seasonal decadal trends in sea level may also more strongly diverge in the future if the large-scale hy- drological cycle is intensified in a future climate. This effect may be smoothed if the impact of river regulation becomes stronger in the future. Therefore, detection and attribution studies focusing on identifying the anthropogenic signal (due to greenhouse gas forcing) on the Baltic Sea level would need to consider the spatial and seasonal heterogeneity of the at- mospheric and oceanic drivers of decadal sea-level variations The role of the Atlantic Multidecadal Oscillation appears to also be limited in the Baltic Sea sea-level variability in the historical record compared to other regional drivers. This im-
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the WAIS Divide display strong differences in the recorded isotope variability despite their rather small spatial separa- tion (Gregory and Noone, 2008), which is suggested to be related to slow variations in the dominant circulation pat- terns. While such differences have not explicitly been re- ported for the core sites west of the divide studied here, we hypothesise that signatures of sea-ice variations or merid- ional inflow could affect the isotopic composition at the in- dividual firn-core sites to different extents. This is motivated by model-based results (Noone and Simmonds, 2004) link- ing an increase in sea-ice cover to an earlier ascent of long- range transported air masses to the continent, reducing the influence of local air from the surface, while less sea ice in- hibits this early ascent and allows more mixing of surface air. Variations in sea ice could thus influence the isotopic com- position of air masses by controlling the influence of local moisture, which might only be recorded by certain WAIS cores depending on the elevation of the air mass transport and characteristics of the core positions, such as their surround- ing topography, elevation, or distance to the coast. Decadal trends or slower changes in these variations could then de- stroy the recording of the large-scale coherent temperature field by the firn cores and thus cause a loss in spectral sig- nal power in the isotope record towards longer timescales and an increase in the noise level (Fig. 2c, d). In contrast, the East Antarctic Plateau including the DML firn-core sites is higher in elevation and might be more shielded from ma- rine influences by the steep topographic slopes leading to a more coherent signal. This might also explain, besides differ- ences in core quality, the rather low agreement among deep West Antarctic cores on millennial timescales compared to East Antarctic cores (WAIS Divide Project Members, 2013). However, since our inferences are speculative, further stud- ies are needed to help disentangle the role of variations in West Antarctic temperature, atmospheric circulation and sea ice on the recorded isotope variability.
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P. tarapacana populations located in relatively close prox- imity to the study lakes. Cross-sections and wedges were ob- tained from dead and living trees. From a total of seven sam- pled sites, we selected those that were highly replicated (> 60 tree-ring series), contained at least 300 years of records, and were highly correlated with lake area fluctuations (r > 0.5; Table 1). Two sites met these criteria: the tree-ring records from the Granada and Uturunco volcanoes (Table 1). The Schulman’s convention (1956) for the Southern Hemisphere was used to assign to each ring the date of the calendar year in which radial growth starts. We measured total ring width to ± 0.001 mm and cross-dated samples using standard proce- dures (Stokes and Smiley, 1968; Fritts, 1976). The program COFECHA (Holmes et al., 1983) was used to detect mea- surement and cross-dating errors. The ring-width chronolo- gies from Granada and Uturunco shared large percentage of common signal in tree-ring variations (r > 0.62; 1620–2006 common period), therefore, 155 tree-ring series were merged into a regional chronology. Ring-width measurements were standardized to remove the age-related growth trends and minimize the growth variations not related to climate (Fritts, 1976). A negative exponential curve or straight line was used to standardized ring-width series. When this standard- ized growth trend is removed, some of the variance related to climatic forcing signal can also be removed, leading to a trend distortion in resulting index series (Melvin, 2004). To prevent trend distortion the signal free detrending procedure (Melvin, 2004; Melvin and Briffa, 2008) was applied to the tree-ring measurements. The regional tree-ring chronology was produced with the RCSigFree program (Tree Ring Lab- LDEO, Columbia University http://www.ldeo.columbia.edu/ tree-ring-laboratory/resources/software).
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Tanzania has inadequate weather stations (28-synoptic weather stations), which are sparsely distributed over complex topographic terrain. Many plac- es, especially rural areas, have no stations to monitor weather and climate. In this study, we evaluate the performance of ENACT-MAPROOM products over Tanzania with the aim of assessing their potential to supplement ob- served weather and climate data, especially over areas where there is limited number of weather stations. Monthly rainfall total and monthly averaged minimum and maximum temperatures from ENACT-MAPROOM are eva- luated against observed data from 23 weather stations. The evaluation is li- mited to analyze how well the ENACT-MAPROOM products reproduce cli- matological trends, annual cycles and inter-annual variability of rainfall, minimum and maximum temperatures. Statistical analysis recommended by the World Meteorological Organization (WMO) that includes that correla- tion and trend analysis are used. It is found that ENACT-MAPROOM prod- ucts reproduce the climatological trends, annual cycles and inter-annual va- riability of rainfall, minimum and maximum temperatures over most sta- tions. The statistical relationship between ENACT-MAPROOM products against observed data from 23 weather stations using Pearson correlation coefficient indicates that ENACT-MAPROOM products bear strong and sta- tistically significant correlation coefficient to observed data. The overall eval- uation here finds high skills of ENACT-MAPROOM products in representing rainfall and temperature over Tanzania, suggesting their potential use in planning and decision making especially over areas with limited number of weather stations.
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generally shallower but within ⇠ 50 % of the actual simulated LH to LGM and LH to LIG temporal gradients and temporal gradients calculated from multi-decadal variability are generally shallower than both spatial and actual simulated gradients. The largest sources of uncertainty in salinity reconstructions are found to be caused by changes in regional freshwater budgets, ocean circulation, and sea ice regimes. These can cause errors in salinity estimates exceeding 4 psu. Our results suggest that paleosalinity reconstructions in the South Atlantic, Indian and Tropical Pacific Oceans should be most robust, since these regions exhibit relatively constant δ Ow -salinity relationships across spatial and temporal scales. Largest uncertainties will
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behaviour. Nowadays, it is well known that the Earth’s rate of rotation (), and hence the length of the day (LoD), is not constant in time, but exhibits changes of the order of a few parts in 10 8 , i.e. δ/ ∼ 10 −8 . Though variations with the largest amplitude occur over the decadal timescale, ob- servations of the evolving state of Earth rotation have re- vealed the occurrence of variability on many timescales rang- ing from days to centuries and longer. This wide range of timescales is the expression of the wide variety of processes influencing Earth rotation. These processes involve external tidal forces, superficial processes concerning the atmosphere, oceans, and hydrosphere, and internal processes taking place at the core-mantle boundary as well as within the solid Earth itself (Hide and Dickey, 1991). This is the reason why, for many years, scientists in the field of astronomy and Earth sci- ences have been focusing their attention on the interpretation of the Earth rotation fluctuations as a key to understanding fundamental terrestrial dynamical processes.
Microfacies and time series analyses from an annually lam- inated sedimentary archive of the Holsteinian interglacial (MIS 11) yield a strong signal of natural cyclicity at decadal and sub-decadal time scales. The decadal-scale cyclicity is attributed to solar forcing that may have influenced the sedimentation of the light varve sub-layers (spring/summer) by driving changes in the productivity of the palaeolake. The sub-decadal-scale cyclicity is attributed to ENSO and NAO climate modes, predominantly influencing the dark sub-layer formation (autumn/winter) through changes in atmospheric circulation that affected lake mixing. Our analyses clearly demonstrate that in order to interpret the signals of varve time series analysis and to correlate them with temporal modifica- tions of the external climate forcing, it is essential to (a) un- derstand the sedimentological processes controlling varve formation and to (b) compare the results of individually ana- lyzed seasonal sub-layer-thickness datasets.
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and rivers of the Northern hemisphere (Driscoll et al., 2003; Frey and Smith, 2005; Monteith et al., 2007; Erlandsson et al., 2008; Haaland et al., 2010), including Latvian rivers as well (Klavins et al., 2012). Our data show that despite low data quality and short period of observations (1980–2003), statistically significant upward trends of color values can be found in both rivers (Table 3). However, a closer look at ob- tained curves indicates also an oscillated character of this process in both cases (Fig. 3). The drivers behind brownifica- tion are heavily debated and have been ascribed to hydrolog- ical factors (Hongve et al., 2004; Erlandsson et al., 2008), as well as an increase in temperature, changes in land-use and reduced acid deposition (Correll et al., 2001; Freeman et al., 2001; Monteith et al., 2007).
We note a few caveats and possibilities for improvements to this study. We only investigated the volcanic response to different initial conditions of the NAO and PDO. Therefore, our simulations in this study should be extended with ex- periments starting with other initial conditions like the re- cent El Niño year 2015–2016. Another factor currently ne- glected is the phase of the QBO as it changes due to the post- volcanic atmospheric response (e.g., Thomas et al., 2009) and its self-modulation by strong volcanic eruptions (Aquila et al., 2014). The model (MPI-ESM) in the low-resolution version used in this study is not able to develop its own quasi- biennial oscillation (QBO), but the same model with higher vertical resolution shows a predictive skill of the QBO of up to 4 years (Pohlmann et al., 2013). Another aspect is that our results could be model dependent and the analysis should be expanded to a multi-model study. In order to gain a better understanding of the impact of volcanic eruptions on decadal predictions and predictability, a collaboration is planned be- tween the model intercomparison project on the climatic re- sponse to volcanic forcing VolMIP (Zanchettin et al., 2016) and the decadal climate prediction project DCPP (Boer et al., 2016). In line with the protocol of the upcoming CMIP6 (Eyring et al., 2016), a set of decadal prediction experiments will be conducted in which, similar to our experiment, the impact of a Pinatubo-like eruption occurring in 2015 will be examined, which provides the unique opportunity to discuss our results in a multi-model framework.
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First, spring, summer and annual simulated river flows by SIM are compared to observations, at the interannual level (1961–2012 period) and after low-pass filtering (1970–2003 period), in order to assess the ability of SIM to capture ob- served river flow variations (Fig. 11). For the vast majority of the stations studied here, the model captures the interan- nual variability in river flows well. Correlations lower than 0.70 are seldom found. Regarding low-frequency variations, the model also performs well, except at a few stations, espe- cially in summer. It is not clear whether deficiencies in the hydrological model, direct anthropogenic influences on river flows or some measurement issues explain those poor cor- relations. Note that as SIM does not take into account direct human influence on river flows (dams, pumping, etc.) the fact that simulated river flows are most of the time consistent with observed river flows suggests that direct anthropogenic influ- ences are not dominant in the interannual and multi-decadal fluctuations in the period simulated here for most stations. Note that a much more complete evaluation of SIM is given in Habets et al. (2008). As low-frequency variations in river flows are generally well simulated, and as the precipitation forcing is derived from observations, the previous analysis reinforces our confidence in the realism of low-frequency variations in the other components of the continental water cycle (evapotranspiration, SWI) simulated by SIM, which is strongly constrained by precipitation and river flows because
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This paper is focused on the analysis of December temperature variability with emphasis on spatial distribu- tion and its regional detail in Pakistan. Most of climatic studies are supported by “time series figures of temper- ature”, while rarer studies map the anomalies and also neglect the spatial distribution of the observations. In this paper, we mapped the isotherms through which we learned about the shifts of average temperature at regional scale and recognized their anomalies by simulating temperature coefficient at decadal scale.
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The scientific analysis of the DCPP is predicated on its broad multi-system approach. In particular, the extensive archive of multi-system results is a resource for the analysis commu- nity, and many novel and innovative analyses will be under- taken based on the availability of these data. The improve- ments in the design of the Component A hindcast exper- iment, the broader participation compared to CMIP5, and the augmented archive of results provide the basis for many types of analyses. The most obvious analysis results for Component A hindcasts are measures of historical forecast skill on annual, multi-annual and decadal forecast ranges for each system and, ultimately, for an optimum combination of these results into a multi-system forecast. The skill of the ini- tialized forecasts compared to the results of historical climate simulations is a measure of the impact of initialization and is certainly of interest. These analyses require the bias correc- tion of the forecasts, a version of which is as discussed in Appendix E. No one measure can convey all of the verifica- tion information available from a set of hindcasts/forecasts (e.g. Jolliffe and Stephenson, 2011); nevertheless, there are basic measures that can be used as suggested in Goddard et al. (2013) and in the Standard Verification System for Long- range Forecasts from the World Meteorological Organization (Graham et al., 2011).
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