The impact of rising greenhouse gas emissions and changing land use on climate can be simulated using global climate models (GCMs). However, since GCMs are computationally expensive to run, long climate simulations are currently feasible only with horizontal resolutions of 50 km or coarser. Since climate fields such as precipitation and wind speed are closely correlated with the local topography, this resolution is inadequate for the simulation of the detail and pattern of climate change on the scale of a region the size of Ireland. To overcome this limitation, the RCM method dynamically downscales the coarse information provided by the global models and provides high-resolution information on a subdomain covering Ireland. The computational cost of running the RCM, for a given resolution, is con- siderably less than that of a global model. The RCMs of the current study were run at high spatial resolution, up to 4 km, thus allowing a better evaluation of the local effects of climate change. Since RCMs have a better representation of coastlines and general topography, the resulting model output is more useful for focused cli- mate impact studies. An additional advantage is that the physically based RCMs explicitly resolve more smaller scale atmospheric features than the coarser GCMs. In this work, projections for the future Irish climate were generated by embedding or “nesting” two RCMs within a set of GCM simulations, and so providing high-resolu- tion local detail over Ireland. The RCMs used in this work are the COnsortium for Small-scale MOdeling–Climate Limited-area Modelling (COSMO-CLM) model and the Weather Research and Forecasting (WRF) model. The GCMs used are the Max Planck Institute’s ECHAM5, the UK Met Office’s HadGEM2-ES (Hadley Centre Global Environment Model version 2 Earth System con- figuration), the CGCM (Coupled Global Climate Model) 3.1 from the Canadian Centre for Climate Modelling and the EC-Earth consortium GCM. Simulations were run for a reference period 1981–2000 and future period 2041–2060. Differences between the two peri- ods give a measure of climate change. The future
88 Read more
2. Cooling speed up: Heating is the major energy use for comfort at the final energy consumption level in China. Heating increases more than 3 times during the projection period, and energy saving standards damp down the energy use in heating significantly. However, little has been done for cooling. When considering primary energy and CO2 emissions, cooling demand will soon become as important as heating. Cooling uses only 2.57 Mtce of total energy in 2000, but the value approaches the level of 20 Mtce in 2030, which is almost 8 times of the year 2000’s value according to the projections. Yao et al. (2005) indicate the similar result of the rise in cooling intensity. Current energy saving policies have only a minor impact on saving energy use in cooling. Hence traditional building standards and subsidiary measures have to be strengthened to foster an efficient use of energy for cooling purposes.
31 Read more
Note that in the context-dependent setting, the number of flat logical forms (Model C) still in- creases exponentially with the number of utter- ances, but it is an overwhelming improvement over Model A. Furthermore, unlike other forms of relaxation, we are still generating logical forms that can express any denotation as before. The gains from Model B to Model C hinge on the fact that in our world, the number of denotations is much smaller than the number of logical forms. Projecting the features. While we have defined the space over logical forms for Models B and C, we still need to define a distribution over these spaces to to complete the picture. To do this, we propose projecting the features of the log-linear model (1). Define Π A→B to be a map from a
10 Read more
models to this problem; some have claimed that, due to large uncertainties, climate models are not suitable for as- sessing water resources, and that it is not realistic to expect the level of accuracy required for adaptation-type analysis (Kiem et al., 2011; Kundzewicz et al., 2010), while oth- ers argue that model projections of future climate change are still useful despite their large uncertainties. Many stud- ies have assessed water resources using projections from cli- mate models to drive rainfall-runoff models, thus estimating runoff and stream flow. Conceptual watershed models are often believed to be useful in assessing the impacts of cli- mate change on regional hydrology (Arnell, 1999; Zhang et al., 2007). Gleick et al. (1987) developed a monthly water- balance model specifically for climate change impact assess- ment and addressed the advantage of using conceptual water- shed models in practice. Loukas et al. (1996) applied a UBC watershed model to study the effects of climate change on the hydrological regime of two climatically different British Columbia watersheds, the Upper Campbell and Illecilewaet watersheds. Huntington et al. (2006) investigated the rela- tionships between mean annual temperature, annual precip- itation and evapotranspiration (ET) for 38 forested water- sheds in New England, USA, and concluded climate warm- ing could reduce runoff significantly. Roger et al. (2005) estimated the sensitivity to climate change of mean annual runoff in 22 Australian catchments using selected hydrologi- cal models, showing how results varied between models. Us- ing ArcGIS Geostatistical Analyst, Fu et al. (2007) devel- oped a method to study the impacts of climate change on re- gional hydrological regimes in the Spokane River watershed. This study indicated that a 30 % increase in precipitation could result in a 50 % increase in stream flow; conversely, a 20 % decrease in precipitation could results in a 25–30 % reduction in stream flow. Using a NAM model, Hans et al. (2006) investigated the influence of climate change on river discharge in five major Danish rivers for the future pe- riod of 2071–2100, finding a 12 % increase in runoff. In China, Li et al. (2008) projected an approximately 5 % de- crease in runoff for the headwater region of the Yellow River with an improved XAJ model and ensemble projections of Global Climate Models (GCMs) used in IPCC AR4. Zhang et al. (2009) assessed hydrological responses of the Yellow River to hypothetical climate scenarios using a snowmelt- based water balance model, demonstrating the role soil and water conservation measures could play in reducing the sen- sitivity of runoff to climate change.
10 Read more
Abstract. Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emis- sions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of in- creasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact mod- els, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact- model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
14 Read more
doubt on the validity of our results for regions like the North Atlantic. In a recent study, (Pardaens et al., 2011) compared the spread in sea level projections (for which OHC relates directly with the thermosteric component) between two dif- ferent ensembles within the QUMP (Quantifying Uncertainty in Model Projections) project, and found comparable results for the experiments with and without flux adjustments. That same study included a model intercomparison of SL in fu- ture projections, in which ECHO-G shows a great degree of coherence with the mean model ensemble, reproducing most of its different local features, like a SL dipole in the North Atlantic, a strong meridional gradient in the Southern Ocean with negative changes near 60 ◦ N and positive further north, and positive SL anomalies in the North Pacific, these latter two being coherent with the larger OHC700 changes in the extratropics found herein. In a different intercompar- ison study (Fern´andez-Donado et al., 2012), the simulations FOR1 and FOR2 show values for the equilibrium climate sensitivity, the transient climate response and the temperature change in the MCA-LIA transition well within the range of the other millennial simulations, most of them not using flux corrections. However, it is important to note that ECHO-G stands out to have the smallest heat uptake efficiency among all the CMIP3 and CMIP5 models (Kuhlbrodt and Gregory, 2012). In either case, it is not clear what this implies in terms of the reliability of the model, as the same analysis states that models tend to overestimate this value. Indeed, ocean stratifi- cation (which controls how fast heat is transported downward and thereby contributes to the heat uptake efficiency) is rather realistic in ECHO-G when compared to observations and the ensemble of CMIP-3 and CMIP-5 simulations. To conclude, the ECHO-G overall performance, despite the flux correc- tions, seems comparable to that in other AOGCMs. However, evaluating the validity of our results in other model simula- tions is an important subject for further work.
19 Read more
Assessing the importance of capturing secular trends To assess the degree to which these efforts to model trends in demographic and socioeconomic conditions could alter model projections, we compared the chosen model to a static model equivalent that included all model components but did not include changes over time in any of the demographic or socioeconomic in- puts; that is, fertility, mortality, migration or educational attainment parameters were held fixed at their starting- year values, as is the current standard approach [12–15]. We first compared the two models over the period 1992 to 2010, contrasting their urban- and rural-specific population size estimates with observed data . We next compared the two models starting from the year 2010 and projecting a further 15 years into the future, in order to characterize the degree of divergence between the two sets of model estimates (fixed and with trends) and independent United Nations population projections . We finally compared life expectancy estimates from the two models in terms of both historical (1992–2010) and future projections (2010–2025), and contrasted pre- dictions from the models for the impact of a simulated intervention: efforts to increase the level of educational attainment achieved by rural women, which would have provided universal primary education to rural females in the year 2000 . Prior intervention studies (i.e., cluster randomized trials and natural experiments) have estab- lished that increasing primary education availability to women lowers mortality through a number of complex mechanisms such as reducing early marriage and associ- ated premature fertility that increases the risk of mater- nal mortality [35, 36]. We increased the educational attainment rates among rural females to simulate univer- sal primary education in 2000, comparing the resultant estimated life expectancy differences between the two models over subsequent years.
17 Read more
Remaining challenges in assessing future Greenland Ice Sheet changes include (1) characterizing model response to parameter choices, (2) establishing an initial state for prog- nostic simulations, and (3) matching data on the ice sheet’s past behavior (van der Veen, 2002; Heimbach and Bugnion, 2008; Aschwanden et al., 2009; Stone et al., 2010; Greve et al., 2011). Ice sheet models have many uncertain parameters, and the choice of parameter values has a strong influence on modeled behavior (Stone et al., 2010; Greve et al., 2011). Because the thermal field within the ice sheet is mostly un- known (cf. Greve, 2005), ice sheet models are “spun up” to the present using reconstructed former surface temperatures and sea levels. Most models are tuned to produce an accept- able match to the modern geometry of the ice sheet (e.g. Ritz et al., 1997; Stone et al., 2010; Greve et al., 2011; for fits to paleo-data, see Tarasov and Peltier, 2003; Lhomme et al., 2005; Simpson et al., 2009). Achieving a good fit between the modeled and observed ice thickness distributions at the end of model spinup is challenging (Aschwanden et al., 2009; Greve et al., 2011), and simulated ice volumes at the end of spinup runs are generally larger than expected (e.g. Heim- bach et al., 2008; Stone et al., 2010; Robinson et al., 2010; Vizcaino et al., 2010; Greve et al., 2011; cf. Bamber et al., 2001). Finally, data on past ice sheet variations (e.g. Alley et al., 2010, and references therein) provide a check on ice sheet models. If a model reproduces past changes well, then we can have more confidence in its projections of future changes (cf. Oreskes et al., 1994).
18 Read more
The CMIP5 multi-model ensemble has more than twice as many models and many more experiments compared with the CMIP3 ensemble. It might not al- ways be practical to use all climate models from the CMIP5 ensemble in a particular impact assessment study, as substantial resources and computer time are required for evaluation of each climate scenario. To assist with the selection of GCMs for a specific impact study in a region of interest, we computed a CSI for each GCM incorporated into LARS-WG for 21 re - gions as defined in Giorgi & Francisco (2000) (our Table 3). CSI is de fined as the spatial average (calcu- lated over a region land-mask only) of differences be- tween mean values for the future, 2080−2100, for RCP8.5 and mean values for the CMIP5 baseline, 1995−2005. CSI was computed for mean air tempera- ture calculated as differences in temperatures (°C), and for precipitation calculated as a relative change in precipitation total (%). Fig. 1 presents CSIs for the Mediterranean Basin (MED) and Northern Europe (NEU) for 18 GCMs. All GCMs predicted an increase in annual precipitation in NEU (by up to 25% for MIROC-ESM), and a decrease in annual precipitation in MED (by up to −36% for IPSL-CM5A-MR). Changes in mean annual temperature were similar for both re gions, NEU and MED, and varied from + 3.1°C for INMCM4 to + 6.6°C for MIROC-ESM. An- nual CSIs for 21 regions and 18 GCMs are presented as heat maps in Table 4 for temperature and in Table 5 for precipitation.
18 Read more
annual glacier mass balance and discharge in the Beas River at the Thalout station are both used for model calibration. There is an intra-regional variability of individual glacier mass balance in High Mountain Asia (HMA) as illustrated by Brun et al. (2017). From their study, the glacier mass balance is − 0.49 ± 0.2 annual meter water equivalent (m w.e. a −1 ) in the Spiti-Lahaul region (where the Chhota Shigri Glacier is located) during 2000–2008 based on ASTER DEM differ- encing and 0.37 ± 0.09 m w.e. a −1 in the western Himalayan region from the RGI Inventory during 2000–2016 based on ASTER. Besides, a detailed map of elevation changes during 2000–2011 in the Spiti-Lahaul region based on the SPOT5 DEM is provided in the study of Gardelle et al. (2013), which showed that the changes in the glaciers in the upper Beas basin are quite similar to the changes in the Chhota Shi- gri Glacier during 2000–2011 in general, although there is variability both within single glaciers and over the region. Furthermore, the glacier mass balance time series published in the Spiti-Lahaul region (where the upper Beas basin is located) available for comparison are for the Chhota Shi- gri Glacier and Bara Shigri Glacier (Berthier et al., 2007). In these the only one covering a sufficient time period to be comparable to our simulation period is the Chhota Shi- gri Glacier (2002–2014), which also has geodetic mass bal- ance data for validation (Azam et al., 2016). In addition, the Chhota Shigri Glacier is a part of the Chandra Basin, which is a sub-basin of the Chenab River basin (Ramanathan, 2011), but it is attached to the northeastern boundary of the upper Beas basin, which is close to Manali and Bhunter stations (Fig. 1). Therefore, we assumed the mass balance data of Chhota Shigri Glacier to be representative of the glacier mass balance of the glacierized area in our basin (see Fig. 1 and Ta- ble 4), which is also used for the glacier module calibration in the study.
21 Read more
realizations were acquired from the data pool in the World Climate Research Programme’s (WCRP’s), Coupled Model Intercomparison Project phase 5 (CMIP5). Models are selected according to the availability of data (availability of hourly rainfall time series as the main constraint) and the relative independence between models. The latter criterion is a necessity in using multi-model ensemble approach, which is the mutual independence between model realizations. Climatic models proposed by various groups could be assumed to be independent to a certain extent; nevertheless, these models may have similar elements or contain similar underlying theories for their parameterizations (Tebaldi and Knutti, 2007). To ensure the preservation of the relative independence among models, whenever multiple or revised versions of similar climate model are available, only a single version of such GCM is used. A single scenario, the RCP 6.0 scenario is adopted since it is an intermediary situation that relates to the median curve of global temperature increase among all considered scenarios.
14 Read more
Abstract. Global climate models project widespread de- creases in soil moisture over large parts of Europe. This pa- per investigates the impact of model resolution on the magni- tude and seasonality of future soil drying in central-western Europe. We use the general circulation model EC-Earth to study two 30-year periods representative of the start and end of the 21st century under low-to-moderate greenhouse gas forcing (RCP4.5). In our study area, central-western Europe, at high spatial resolution ( ∼ 25 km) soil drying is more se- vere and starts earlier in the season than at standard reso- lution ( ∼ 112 km). Here, changes in the large-scale atmo- spheric circulation and local soil moisture feedbacks lead to enhanced evapotranspiration in spring and reduced pre- cipitation in summer. A more realistic position of the storm track at high model resolution leads to reduced biases in precipitation and temperature in the present-day climatol- ogy, which act to amplify future changes in evapotranspi- ration in spring. Furthermore, in the high-resolution model a stronger anticyclonic anomaly over the British Isles ex- tends over central-western Europe and supports soil drying. The resulting drier future land induces stronger soil mois- ture feedbacks that amplify drying conditions in summer. In addition, soil-moisture-limited evapotranspiration in summer promotes sensible heating of the boundary layer, which leads to a lower relative humidity with less cloudy conditions, an increase in dry summer days, and more incoming solar radia- tion. As a result a series of consecutive hot and dry summers appears in the future high-resolution climate. The enhanced drying at high spatial resolution suggests that future pro- jections of central-western European soil drying by CMIP5 models have been potentially underestimated. Whether these results are robust has to be tested with other global climate models with similar high spatial resolutions.
16 Read more
During development, the retinal vasculature grows toward hypoxic areas in an organized fashion. By con- trast, in ischemic retinopathies, new blood vessels grow out of the retinal surfaces without ameliorating retinal hypoxia. Restoration of proper angiogenic directionality would be of great benefit to reoxygenize the ischemic retina and resolve disease pathogenesis. Here, we show that binding of the semaphorin 3E (Sema3E) ligand to the transmembrane PlexinD1 receptor initiates a signaling pathway that normalizes angiogenic directionality in both developing retinas and ischemic retinopathy. In developing mouse retinas, inhibition of VEGF signaling resulted in downregulation of endothelial PlexinD1 expression, suggesting that astrocyte-derived VEGF normally promotes PlexinD1 expression in growing blood vessels. Neuron- derived Sema3E signaled to PlexinD1 and activated the small GTPase RhoJ in ECs, thereby counteracting VEGF-induced filopodia projections and defining the retinal vascular pathfinding. In a mouse model of ischemic retinopathy, enhanced expression of PlexinD1 and RhoJ in extraretinal vessels prevented VEGF- induced disoriented projections of the endothelial filopodia. Remarkably, intravitreal administration of Sema3E protein selectively suppressed extraretinal vascular outgrowth without affecting the desired regen- eration of the retinal vasculature. Our study suggests a new paradigm for vascular regeneration therapy that guides angiogenesis precisely toward the ischemic retina.
13 Read more
The issues of uncertainty in hydrological modelling and hydrological projections due to climate change are not new; there is much research published on this subject in global and regional studies (Todd et al., 2011; Addor et al., 2014; Ab- baspour et al., 2015). However, few of the case studies at a catchment level were trying to assess the influence of uncer- tain future and hydrological parameter uncertainty (Poulin et al., 2011; Bennett et al., 2012; Steinschneider et al., 2012, 2015; Vormoor et al., 2015). In a number of studies the hy- drological model structural and parametric errors are dealt with using a multi-model approach and introducing weights for hydrological model parameter sets following assumed goodness of fit criteria, e.g. in the form of a likelihood func- tion (Wilby and Harris, 2006; Steinschneider et al., 2012; Addor et al., 2014). Addor et al. (2014) concentrated on the influence of different hydrological model structure, involv- ing three hydrological models, emission scenarios, climate models, post-processing and catchments. Their results indi- cate that influence of model structure varies with the catch- ment. However, the authors did not take into account hydro- logical model parameter uncertainty, which is included in the present paper. Following the results presented by Demirel et al. (2013a) the choice of the GCM/RCM has larger influence than the choice of the emission scenario on the projections of low-flow indices. Similar findings for the high-flow in- dices were presented by Osuch et al. (2016). There is general agreement that we cannot avoid uncertainty in climate mod- els (Knutti and Sedlacek, 2012). The question arises as to how large the uncertainty is and if it is acceptable to the end user in adaptations to climate change and flood and drought risk assessments.
14 Read more
Due to linear regression analysis limitations, it was not pos- sible to model all the measurements of blood velocity in TCD. The main limitation in this regard was the available high mul- ticollinearity and large number of variables. Therefore, only the variables from TCD were modeled and were arbitrarily dichotomized to show if the blood velocity was abnormal in the TCD of single vessels. The results of the multivariate regression analysis showed that the baseline UNSS, right and left sided middle cerebral artery involvement, as well as right sided anterior cerebral artery involvement were statistically significant predictors of the UNSS score at six months.
Perfect model tests are most informative when the sim- ilarity of ensemble members is approximately equal to the similarity between observations and ensemble members, in metrics that are relevant to the calibration process and appli- cation. For example, if a model-as-truth experiment were per- formed using all CMIP ensemble members, including mul- tiple initial conditions members from the same model, the ensemble calibration process could fit the “truth” simulation much more closely than models are likely to be able to fit observational data. That is, weighting or sub-selection would favour any simulations from the same model as the truth en- semble member, so that the experiment’s success might be misleading. This suggests eliminating obvious duplicates be- fore the perfect model tests (see e.g. Fig. 5 in Sanderson et al., 2017). It is also worth emphasising that the motiva- tion for this process is not to test the weights or ensemble subset as far out-of-sample as possible, but rather to ensure that the calibration process is appropriate for its intended application. Note that biases shared among models, espe- cially those which affect projections, will increase agree- ment among models relative to observations, so that model- as-truth experiments should be treated as a necessary but not sufficient condition for out-of-sample skill.
15 Read more
discharges (together with the other variables of the model). For each scenario (GCMs + LOC) the ensemble simulations (100 trajectories of 10 yr daily rainfall, temperature, and all the daily hydrological variables) required about 1 hour (us- ing MATLAB® 7 on a desktop PC, Windows XP®, 3 Gb RAM, 3.2 Ghz CPU Clock). In Fig. 5, together with the flow discharges as from the deterministic simulation, we re- port the upper and lower bounds (α = 5 %) of the daily flow discharges for each scenario, as obtained by ranking of the values given by the ensemble projections. These provide visually an idea of the expected spread (uncertainty) of the projected discharges. Notice that in few cases, the determin- istic value may lay outside (below, or above) the 5 % confi- dence range from the ensemble simulations. In Table 5 we provide for each scenario the average yearly discharge, as given by the ensembles, Q av,s . Using the confidence bounds
17 Read more
The paper is organized as follows: Section 1, contains a few elementary definitions and results from Banach algebras theory. In this section we introduce the concepts of numerical range and the spectrum and the spectral radius of an element and investigate their properties. In Section 2, we introduce the generalized orthogonal pro- jections, generalized e-projections in Banach algebras and we study some necessary and sufficient conditions for them and their spectrums.
Bias-ratio, 𝑓 𝑐 is calculated over the climate regions and for 4 seasons, results from historical runs are shown in Table 4.2. As we already mentioned, ENC and WNC are the two climate regions that experienced higher magnitude of climate change during JFM. Over these two climate regions bias to original change ratio is around 0.8. Over Northwest and West, amount of spatial bias from equal weighting are 0.53 and 0.64 of the observed climate change respectively. During AMJ when observed changes are lower compared to JFM, bias-ratio decreases. Equal weighting improves performance with bias-ratio equal to 0.60 and 0.59 over WNC and ENC respectively. Equal weighting performs better over Southwestern US, with bias-ratio close to zero which signifies correlated error in GCM climate change projections are negligible over SW during AMJ. During JAS, the bias-ratio exhibits negative values for 8 out of 9 NCDC climate regions. Climate models are overestimating the change in mean of seasonal temperature. Finally, during OND the spatial bias after equal weighting remains even higher than the observed changes. Over Southeast and Central, where mean seasonal temperature is decreasing, spatial bias in projections are 1.5 times bigger than the observed climate change. Over Western US, the bias-ratio is 2.58 which denotes a very high amount of bias that could not be eliminated by equal weighting alone, unless a proper bias correction is applied along with multimodel combination of climate change projections. We used the term- 𝑒 𝑚,𝑐 ,
147 Read more
Proving: Each tensor of second rank can be divided into symmetrical and anti-symmetrical part . The symmetrical part is presented in three-dimensional space with tri-axial ellipsoid whose projections on the coordinate planes 0xy, 0yz, 0xz have higher average densities from the projections of the ellipsoid on the plains of its own coordinate system: 0ab, 0bc, 0ac, where a, b, c are its semi-axles. Anti-symmetrical part of the tensor is represented by a vector, for which we have already proved that its projections on the coordinate axes have density greater or equal to the average density.