5. Conclusions and discussion
The SH surface westerly wind stress, a major driver for SO circulation and water mass transformation, is predicted to increase in the future. The recent modeling study by Munday and Zhai (2017) shows that the sen- sitivity of SO circulation to wind stress changes depends strongly on how these stress changes are brought about (i.e., by changes of the mean wind or wind variability). Although the enhancement of wind stress due to the inclusion of wind fluctuations is consistent across re- analysis products, the diagnosis of trends is not consis- tent ( Lin et al. 2018 ), which may have important implications not only for SO circulation but also globalocean heat and carbon uptake. CMIP5models are widely used to simulate historical and predict future changes of the SH westerly winds, SO circulation, and water mass properties. However, the role of wind fluc- tuations in determining the strength of SO wind stress in CMIP5models has not been quantified before. In this T ABLE 3. Significant trends of SO wind stresses (10 24 N m 22 yr 21 ) in 11 CMIP5models over the period of 1960–2005 calculated from model output wind stress (tauu) and t .6hr , as well as trends of t .yr (10 24 N m 22 yr 21 ) and kinetic energy (10 22 m 2 s 22 yr 21 ) in these 11 models. Trends not significant at the ,5% level by a two-sided t test are marked with a slash (/).
For the terrestrial indicators (section 2.2), damage functions are constructed using climatescenarios from 23 atmosphere-ocean general circulation model patterns scaled, using ClimGEN ( Osborn et al 2016, 2018 ) , to de ﬁ ned increases in global mean temperature and applied to a reference period clima- tology. These scaled scenarios were constructed using pattern-scaling rather than time-slicing (James et al 2017) in order to eliminate the effects of year-to-year and decade-to-decade variability on differences between scenarios at different levels of warming. Twenty-three patterns for each climate variable ( pre- cipitation, precipitation variability, temperature, humidity and net radiation ) were constructed from 23 CMIP5climatemodels ( listed in supplementary mat- erial), allowing the construction of 23 damage func- tions for each indicator, time period and socio- economic scenario. All 23 patterns are assumed to be equally plausible and independent. The changes in monthly climate were applied to the CRU TS4 0.5°×0.5° 1981–2010 climatology (Harris et al 2014) using the delta method in ClimGen ( Osborn et al 2016, 2018 ) to produce perturbed 30 year time series representing speci ﬁ c increases in global mean surface temperature. For precipitation, changes in monthly variability projected by climatemodels are also diagnosed and used within ClimGen to perturb the observed variability to represent the increased or decreased variability simulated by each climate model (see Osborn et al 2016, for more details). This is
We focused our study on 243 bird species occurring within the northern tropical savanna woodlands excluding water- birds and rainforest species that may occur intermittently in savanna regions. Bird occurrence records were collated from the Birds Australia Atlas (Blakers et al. 1984; Barrett et al. 2003), the Queensland Governmental atlas WildNet (Envi- ronmental Protection Agency 2004), and CSIRO (protocol as in Reside et al. 2010). The mean number of records per species was 23,027 (range: 6–34,330), and the occurrence records spanned from 1950 until 2009. Species were grouped according to their movement life history (migratory, no- madic, sedentary, partially migratory, and species that were both nomadic and sedentary). Most species that occur within Australian tropical savannas also occur beyond the savanna region, many occurring widely across Australia. The species were grouped into five broad biogeographic groups describ- ing their broader range: arid, Cape York Peninsula, temper- ate, tropical, and ubiquitous for species that encompassed two or more of the above categories; according to the litera- ture (Schodde 1981; Marchant and Higgins 1990; Marchant and Higgins 1993; Higgins and Davies 1996; Higgins 1999; Higgins et al. 2001; Higgins and Peter 2002; Higgins et al. 2006). Details for each species are provided in the Support- ing information. While we focused our study on the suite of species that occur in the tropical savannas, we investigated the effect of climatechange on species’ broader ranges, even when they extend beyond the savanna and across the rest of Australia. Detailed explanations of the biogeographic group- ings can be found in Reside et al. (2010). Species conservation status was also compiled. Nineteen of 243 species in our study are listed as having a significant conservation status under the Australian Commonwealth Government (EPBC: Environ- ment Protection and Biodiversity Conservation Act 1999),
An increasing, signi¿cant annual trend was observed in the E;TMNT and E;TM;T; both series are non-stationary. A more pronounced warming was observed in the E;TMNT that can be associated with urbanization. The GEV distribution and GPD were adequately ¿tted to both temperature extremes, but extrapolation with the return periods has some shortcomings. The inclusion of the time trend as a covariable in the location parameter produced a signi¿cant improvement (at 95%) in the GEV, especially for the E;TMNT; therefore, individuals assessing impacts in several areas should use the values shown in Table IV. It can be observed that by the end of twenty-¿rst century the extreme maximum temperature could be 2 to 3º C higher than current, and the winter could be less severe, as the extreme minimum temperature, according to the probabilistic model, suggests increases of 7 to 9 ºC with respect to the base period (1950-2010). Although the GPD uses daily values, it fails to integrate a temporal trend in modeling, which makes its application to climatechange issues questionable.
We carried out an extensive search for species records to limit the impacts of using in- complete distributional data. Following a thorough data quality program, we modelled the present and future climatically suitable areas for 191 species across Europe. We ana- lysed relative changes in species’ climatically suitable areas as well as their potential shifts in latitude and longitude with respect to species’ thermal preferences. Addition- ally, the effects of climatechange on species were analysed by subdividing them into the following ecological and biological trait-based sets: 1) endemic / non-endemic spe- cies and 2) rare / common species within European ecoregions; 3) species with an aquatic larval and a terrestrial adult stage / species with a fully aquatic life cycle; and species groups based on their 4) stream zonation preference and 5) current preference. We hypothesized that climatically suitable areas would shift northwards due to warming temperatures (Chen et al., 2011), and that the extent of climate-change effects would be dependent on species thermal preferences (Domisch et al., 2011). Further, we expected that endemic and rare species would be more threatened by warming climates than the respective counterparts, as specific habitat requirements may not be present under future climate conditions (Malcolm et al., 2006; IPCC, 2007). Similarly we expected that spe- cies with a fully aquatic life cycle would lose more climatically suitable area than spe- cies with an aquatic larval and terrestrial adult stage, as changing precipitation patterns may force the restriction of habitat availability (Xenopoulos et al., 2005). Since species occurring at specific stream zones along the river continuum are expected to respond differentially to climatechange due to different thermal regimes (Hering et al., 2009; Domisch et al., 2011), we expected that cold-adapted headwater species would be more vulnerable to warming climates than thermophilic species distributed along the mid- and lower-reaches of the river continuum. Last, climate warming is expected to result in changes in water availability as well as in stream discharge changes (Milly et al., 2005; Xenopoulos et al., 2005), and we hypothesized that climatically suitable areas for spe- cies adapted to fast running waters would decrease because of expected droughts and alterations in stream flow (Bonada et al., 2007b).
A homogeneity test with the RHtest V3 software (Wang and Feng, 2010) was applied to identify possible change points or structural changes in the annual extreme data series of maximum and mini- mum temperatures. The homogeneity test is based on a two-phase regression model with a linear trend for the entire series. This test identified one change point in the maximum temperature in 1989, and two change points in the minimum temperature, the first in 1977, and the second in 1991. Unfortunately, we did not have the station history metadata, so it is not possible to document the origin of these changes. The last change is likely to be due to relocation of the station according to the weather station chief of Mexicali. The new location is 50 m away from the previous one, without any change in altitude. In the current study, no attempt was made to adjust the maximum and minimum temperature series, because regardless of an artificial change in the recorded values in this weather station, positive temporal trends of temperature have appeared at regional level (García-Cueto et al., 2009).
Results of this investigation show that climatechange has the potential to produce a substantial increase in temperature-related mortality in most regions. Figures show a steep rise in heat-related excess mortality that, under extreme scenarios of global warming, is not balanced by a decrease in cold-related deaths. However, the predicted impacts show a strong geographical variability. Some temperate areas such as northern Europe, east Asia, and Australia, are characterised by a relatively small projected warming and increase in heat-related mortality. In these regions, the cold component remains higher and the net change would be smaller than in the other regions studied. By contrast, all the other regions are projected to experience a strong surge in heat-related excess mortality, while the cold component becomes progressively less important. The net impact seems to be stronger in warmer areas of America and Europe, and particularly in places with tropical climates such as southeast Asia. Notably, arid or equatorial regions, although under-represented in our dataset, include a large proportion of the current and projected global population, and will contribute greatly to the global impact of climatechange.
A software was developed in the framework of the GEOWOW project for compu- ting the mean of the output of an ensemble of climatechangemodels from the World Climate Research Programme (WCRP) Coupled Model Intercomparaison Project Phase 5 (CMIP5). The ensemble mean for the time projections of the Sea Surface Temperature (SST) underclimatechange and the corresponding climatol- ogy were computed: this paper describes the data set and its properties. The gen- erated datasets are of interest for ecologists willing to assess future changes of marine ecosystems, and can be used under Creative Common Attributions license.
5. The observed increase in global carbon emissions over the past 15–20 years has been consistent with higher scenarios (very high confidence). In 2014 and 2015, emission growth rates slowed as economic growth has become less carbon-intensive (medium confidence). Even if this trend continues, however, it is not yet at a rate that would meet the long-term temperature goal of the Paris Agreement of holding the increase in the
tance of the deep upwelling and mixing fluxes for the oxy- gen and phosphorus cycles. As described in Gnanadesikan et al. (2004) this model produces reasonable simulations of temperature, salinity, oxygen, phosphorus, radiocarbon, and particle export when compared with data. Figure 9 shows the budget of oxygen and phosphorus between 30 S and 30 N and 220–1130 m in this model. Advective fluxes are shown with solid arrows, diffusive fluxes with dashed arrows and biological sources and sinks with italicized bold numbers. Note that the vertical fluxes are not necessarily due to di- apycnal processes, isopycnal flows which exchange oxygen- rich and phosphate-poor surface water with oxygen-poor and phosphate-rich intermediate water effectively transport oxy- gen in the vertical. As can be seen in the bottom panel of Fig. 9 the upwelling of deep waters acts as an important source of oxygen for the intermediate depths, accounting for approximately 36% of the total oxygen demand. The up- welling also serves as a source of phosphate to the region, equivalent to 60% of the phosphate source. This implies that a slowdown in the circulation would reduce mid-depth phos- phate concentrations (and thus presumably the biological cy- cling of phosphate within the tropics) by a larger fraction than it would decrease oxygen. Circulation changes brought on by global warming might, therefore, lead to an increase in mid-depth oxygen, countering some fraction of the decrease which would be caused by decreased oxygen solubility asso- ciated with warming. Further investigation of this, however, will have to await detailed analysis of term budgets in full Earth System Models.
Abstract Possible effects of climatechange on floods magnitude and effects are discussed in this document based on existing data and projected changes in precipitation until 2099. This methodology is applied to Matucana Village, which suffers the effects of floods and debris flows. First, historical peak precipitation, fitted to Gumbel distribution, was used, After that, percentage projected changes of precipitation were used to obtain the new mean precipitation to each period 2010–2039, 2040–2069 and 2070–2099; these mean precipitations define a new Gumbel distribution for every time period. Then, projected maximal precipitations to 100 years of return period are estimated and the corresponding peak flow hydrographs were built. Finally, hazard maps are plotted. This application is possible because Matucana is located in a climatologically homogeneous basin. The final results suggest an important increase in magnitude and affected area by floods in the next 90 years under the A1FI emission scenario.
Use of the advection-diffusion model of Eq. (1) to calculate the average ocean heat content and associated changes in sea level is supported by the results shown in the previous sec- tion. The major evidence comes in the agreement with the heat content 0–700 m calculated from the average sea surface temperature by the direct estimates available for the recent past (Domingues et al., 2008; Levitus et al., 2009). The sea level change is also in very good agreement with the earlier estimate for 0–700 m (Fig. 6). It is remarkable that the agree- ment is obtained without adjustable parameters, using the av- erage values for eddy diffusivity and upward drift velocity determined by Munk (1966). It should also be noted that the diffusivity and drift velocity values were determined from data taken at larger depths, while this analysis indicates that they are also applicable in the 0–700 m range, where most of
constructed using pattern-scaling and the delta method and making specific assumptions about disaggregating monthly climate data to the daily scale: other methods are available. It was assumed that each of the CMIP5climate model patterns of change was equally plausible and independent, and can be matched with any increase in global mean temperature. A different ensemble of model runs – for example using higher resolution models – could give a different spread of results. The sea level rise scenarios use an empirical relationship between accumulated temperature and sea level rise tuned to results from the IPCC AR5, and assume that sea level rise is globally-uniform. The study uses a series of indicators that represent the consequences of climatechange for future hazards and socio-economic impacts, but do not describe actual impacts: these will depend on current and future adaptation decisions. Different global-scale studies have used different indicators of similar
To investigate regional heat balances, we need to look at ocean heat transports. If we consider the 17-year heat bud- get as a function of latitude, Fig. 4a shows the global merid- ional ocean heat transports integrated southward from 90 N. The red line shows the actual heat transport calculated from 5 day model velocity, and temperature fields with the pink shading around it represent the interannual standard devia- tion in transport at each latitude. The solid blue line is the meridional heat transport based on the surface heat fluxes alone, assuming zero net storage of heat at all latitudes, and the dashed blue line shows the same calculation, with sur- face heat fluxes and data assimilation increments added to- gether. Clearly, assimilation increments are needed to make the surface flux calculation consistent with the direct merid- ional transports (red curve), with the small discrepancies at the southern end resulting from the non-zero storage because the ocean is warming. Heat transports across a number of lat- itudes, based on the ocean section inverse models of Lump- kin and Speer (2007) and Ganachaud and Wunsch (2003), are also shown, with their estimated error bars. The global transport estimates are generally close to the ocean inverse estimates, except in the subtropics 24 N, which we see below is entirely due to the Atlantic.
For every jurisdiction that loses access to a fish stock, another juris- diction likely gains access. This is creating tension over appropriate management responses, whether to intensively exploit a new species to prevent its establishment and potential disturbance of the existing local ecosystem, or whether a more measured approach should be used to allow for the stock ’ s secure establishment (Madin et al., 2012). Moreover, without sufficient understanding of the causes of the apparent “ bounty, ” managers might assume – incorrectly – that the fishery is becoming more productive rather than simply migrat- ing. Here, the risk is that a relaxation of regulations to boost harvest might actually increase the risk of overfishing. Better management would identify the cause of the fishery change and cooperate with partners to manage the stock sustainably as might happen with Ice- land ’ s access to mackerel fisheries that were historically exploited only by Russia, Norway, the Faroes and EU (Hannesson, 2013). Indeed, game theory predicts that international cooperation in treat- ing the stock as a shared resource will likely result in a more sustain- able outcome than each player maximizing their short-term profits without cooperation. In practice, we see that even quasi-cooperation can avoid total destruction expected by game theory as was seen in the Northeast Atlantic mackerel fishery (Hannesson, 2014). Geo- graphic shifts in fish stocks can be monitored in near-real time by creating habitat models that utilize genetic sampling of stock iden- tity, oceanographic remote sensing and satellite tracking of individual fish (Hobday et al., 2014). Cooperation has also been successful at promoting a sustainable outcome in dynamic ocean management. For example, numerous fisheries avoid closure through cooperative reporting of by-catch and dynamic mapping of suitable fishing grounds (Hazen et al., 2013). Similarly, commercial boats avoid ship strikes through voluntary closures or speed restrictions (Maxwell et al., 2015).
to IR64, all PLs had significantly improved adaptation to drought at repro- ductive stage, while 36 PLs also had significantly improved adaptation to drought at vegetative stage. Seventeen PLs had higher yield than IR64 under drought stress, while the remaining 31 PLs yielded at par with IR64 under irrigated conditions. The characterization of these PLs further revealed that dehydration avoidance, efficient partitioning high harvest index (HI) and drought escape (early flowering) together contributed to their improved adaptation to drought. These results indicate that the selection for yield together with some secondary traits under appropriate type(s) of stress and nonstress conditions similar to the target environments are critically impor- tant for improving adaptation to drought without any yield penalty in rice. Adaptation to drought has also been found related to deep root growth and water uptake ability in both upland and lowland rice agroecologies ( Gowda et al., 2011 ). More recently, Gowda et al. (2012) evaluated a set of 20 diverse rice genotypes, identified as Oryza single-nucleotide polymorphism (SNP) panel, for root water uptake ability as a candidate trait for response to drought in greenhouse lysimeter experiments. They detected large geno- typic differences in water uptake and plant growth in response to drought. The total water uptake and water uptake rates correlate with relative root length density, especially at depth below 30 cm, which reveals that response to drought by deep root growth, rather than a conservative soil water pat- tern, is important for lowland rice, with aus rice genotypes showing greatest values for water uptake and root growth. It will be interesting to investigate possible association between root traits and grain yield under drought stress. Rice cultivars such as Sahbhagi Dhan, Sahod Ulan 1, and Tarharra 1, developed through conventional breeding and selections, have been recently released because of their enhanced adaptation to drought-prone environ- ments of India, the Philippines and Nepal, respectively. They are being disseminated to farmers in drought-prone areas in these countries. These cultivars have shown yield advantage of ∼1 t ha −1 under stress ( Mackill et al., 2012 ). Recent study on the new rice for Africa (NERICA) rice culti- vars, developed by crossing Asian rice (O. sativa L.) and African rice (Oryza
moisture parameter set are FC (maximum soil moisture stor- age in millimeter), LP (fraction of FC above which potential evapotranspiration occurs and below which evapotranspira- tion will be reduced) and the coefficient BETA (determining the relative contribution to runoff from a millimeter of pre- cipitation at a given soil moisture deficit). These parameters are dependent on the properties of the catchment, such as the land use type, the wilting point and soil porosity. They will affect the simulated discharge volume. The other parame- ter set includes runoff parameters such as ALFA (measure of the non-linearity for runoff), HQ (the higher flow level at which the recession rate KHQ is assumed) and KHQ (re- cession coefficient at HQ). These parameters influences the shape of the hydrograph (SMHI, 2004). Because of the un- certainty of the parameters, the Monte Carlo Random Sam- pling (MCRS) method is popularly used for parameter esti- mation (Lamb, 1999; Liden and Harlin, 2000) in the cali- bration of the model. However, because the program source code is not available, the above method is difficult to apply in our case. Therefore, quasi-stratified sampling in the form of Latin Hypercube Sampling (McKay et al., 1979) is used. The limited sampling numbers of this method can produce similar results to the Monte Carlo approach (Yu et al., 2001; Murphy et al., 2004). In the previous HBV studies, much experience has been gained in the parameter estimation, which is used to acquire the reasonable ranges of the main parameters in our study (Uhlenbrook et al.,1999; Seibert, 1999; Krysanova et al., 1999; Diermanse, 2001; SMHI, 2004; Booij, 2005). FC ranges from 200 to 500, LP from 0.6 to 1.0, BETA from 1.0 to 5.0, ALFA from 0.8 to 1.1, KHQ from 0.08 to 0.14 and HQ is fixed to 3.0. According to Murphy et al. (2004) and the range of the different parameters, 50–100 sampling numbers are used in the calibration.
Figure 3a,b show the spatial patterns of the ten-year maximum monthly mean WBGT from the NCEP data for 1951–1960 and 2001–2010, respectively. Over this 50-year period, there is an expansion of the regions experiencing WBGT temperatures of over 25 °C, which is mainly across the Pacific region. However, this increase has little impact on the day-to-day activities of acclimatised individuals. Figure 3c–f show the patterns of WBGT for the four RCP scenarios for the last decade of this century. Under RCP 8.5, South East Asia, Latin American, Pacific Islands, India, Northern Australia, Central Africa and the Middle East experience the ten-year maximum monthly mean of more than 30 °C, meaning there are potentially extended periods where the individuals exerting more than 230 W are at risk of heat stress. Many of the affected regions are developing and the emerging economies are likely to be more vulnerable to high levels of WBGT with the adverse implications for human health (due to heat stress and other health impacts) and for socio-economic activity (due to lower labour capacity associated with heat stress). RCP 2.6 shows a significant increase in WBGT with the areas in Central Africa experiencing a maximum monthly mean WBGT of around 27 °C, which means there are some regions for which labour productivity and human health could be an issue.