progressively positive changes in the longwave feedback in warmer climates (positive gray slopes at the bottom of Figure 2), which is primarily attributable to an increase in the water-vapor feedback (Table 1). The temperature feed- back remains nearly constant, whereas the cloud feedback rises in ECHAM6. In the shortwave, the CMIP5 models con- sistently exhibit a negative change of the clear-sky feedback (negative green slopes in Figure 2), likely associated with the vanishing perennial sea ice at the warm temperatures in RCP8.5. This effect alone would decrease the ECS, but the all-sky shortwave feedback, which includes the effect of clouds, is nearly constant in the ensemble mean, despite substantial intermodel spread. It is noteworthy that in the shortwave, the CSIRO-Mk3.6.0 model sticks out from the ensemble with the largest positive all-sky feedback (top pur- ple line in Figure 2), indicating that cloud feedback is likely the cause of the near-runaway warming of that model in the RCP8.5 experiment (Figure 1). Overall, however, the strong variability in changes of the shortwave feedback indicates that these changes cannot play a dominant role in the rise of ECS in the CMIP5 model ensemble.
When it comes to imperfect measurements of uncertainty in IAM, Hennlock (2008b) introduced deeper uncertainty in a IAM approach using robust control in climate-economy modeling. Weitzman independently introduced deeper uncertainty, first in a draft to his Review of the Stern Report, and then in an early working paper of Weitzman (2008). Based on deeper uncertainty, the main results of Hennlock (2008b) and Weitzman (2008) seemed to tell the same story - uncertain probability distributions can justify large measures taken. In Hennlock (2008b) results emerged as a ‘shadow ambiguity premium’, inducing an ambi- guity averse policymaker to take stringent measures (robust carbon pricing). Proposition 6 in Hennlock (2008b) showed that when a policymaker expresses perfect ambiguity aversion his expected shadow carbon cost becomes infinite, and hence, he ‘backstop acts’ by cutting carbon-generating production to zero. Weitzman’s analysis, based on a static linear rela- tionship between a utility function and a parameter with unknown probability distribution, showed that with Bayesian learning in a two-period analysis the result may be an infinite expected marginal utility at zero consumption levels (Weitzman, 2008). 4 In a model with multiple regions, Hennlock introduced deeper uncertainty also in regional damage functions besides climatesensitivity and found an increased stringency from ambiguity aversion as the worst-case beliefs about local damage become dependent on the worst-case beliefs about globalclimatesensitivity. 5
for the Mediterranean ( − 16 %). Overall, half of the simu- lations show robust changes over Europe with large regional differences. Likely changes are found in the south-west of Europe, northern Norway, and the Balkans. It is worth em- phasizing that the differences between globalwarming of 2 and 3 K in low flows are substantial. These changes are on top of those projected between 1971 and 2000 and a 2 K warming, where already 70 % of the simulations show sig- nificant changes (Fig. 5d). As a result, the increase in low flows in the Alpine and Northern regions could, in combina- tion with increased future annual precipitation in the GCMs (see Fig. 4), lead to higher hydropower potential. Conversely, a further decrease in available water (in low flows as well as annual precipitation) in the Mediterranean may pose addi- tional water stress in that area. Although human influences such as reservoir management and human water demand are not considered in this study, different regional adaptation op- tions should be considered depending on whether the world warms 2 or 3 K. This also holds true for the more pronounced warming between 1.5 and 3 K (Fig. 5e and f), where the re- gional changes in low flows as well as the robustness amplify compared to 2 and 3 K warming. These results also highlight the non-linear sensitivity of changes in low flows to different levels of globalwarming. For example with long-lasting in- frastructure or long planning horizons, adaptation strategies should be put in place now whether or not the 3 K level is reached.
There is evidence of both warmer and colder climates in the past. As we look increasingly further back in time, the ev- idence available in the palaeorecord generally becomes both more sparse and less certain, and for this reason it is usually advantageous to focus research on the more recent past where possible. The most recent periods with climates that are sub- stantially different to the present on the global scale have typically been colder than present with large ice sheets over northern continents (i.e. the ice ages). While the Last Glacial Maximum (LGM, 21 ka BP) has been extensively studied, it is challenging to draw inferences from colder climates re- garding our warmer future, in part because of the ice sheets that strongly affect the climate system over large areas of the Northern Hemisphere and which may combine non-linearly with other forcings. Thus increased attention has recently been given to warmer periods (Lunt et al., 2013). These are generally more distant in time, and data are less certain, but the inference from past to future is potentially more robust as the past climate is warmer than present and more simi- lar to what we expect to see in the future, with for example changes in ice sheets being relatively small. It is this infer- ence that the current paper explores. We focus on the mid- Pliocene Warm Period (mPWP), 2.97–3.29 million years be- fore the present, as this represents the most recent time that the atmospheric CO 2 level was substantially higher than in
chosen to vary the strength of the orographic gravity wave drag. For this purpose, we used the GFDL AM2 model with the finite-volume dynamical core [Lin, 2004] at 2° 2° horizontal resolution (hereafter denoted as M45). We vary the parameter gmax in the Pierrehumbert  orographic gravity wave drag scheme, which is a proportionality factor determining the upward base flux of momentum due to the interaction of the wind with orography, from 0.5 to 1.0, and from 1.0 to 2.0. These values lie within a realistic range since the present day climate simulated with these values of gmax are close to observations (not shown). The tuned value of gmax (i.e., the value that produces a simulation of the present day climate that best matches the observed one) is 1.0. In order to save computer time and increase the sample size, this version of the model is run in perpetual winter mode (i.e., insolation and SSTs are set to the January 1 values). The model is spun-up for 5 years with the control value of gmax = 1.0. During these first years, the climate drifts due to an accumulation of snow caused by the perpetual winter conditions. After 5 years, the climate has become stable. Three runs are then branched off, each having a different value for gmax and lasting for another 5 years. These last 5 years are then used to calculate the climatologies. Assuming the atmosphere has a memory of about a month, the 5 years of data are equivalent to a sample size of 60 years. We have verified that the climatology of the present day perpetual winter run is very similar to the December-January-February (DJF) climatology of the pres- ent day run with a seasonal cycle, thus justifying our approach to run in perpetual winter rather than seasonal
3.2 Indices of extreme temperature
Underglobalwarming, the temperature Probability Distribution Function (PDF) of temperature is expected to change, with an increase of the mean value and broadening of its width (increase of variability). This results in an increased probability of extreme events (Fischer & Schär, 2010; Schär et al., 2004). However, the tail of the PDF (i.e., hot and cold extremes) can change, at increasing levels of warming, differently than the mean value. Figure 4 shows the change of selected temperature indices under different warming levels; Figure 5 shows the fraction of land where this change is either robust or non-significant, for NEU and SEU in both winter and summer. TXx, TXn, TNx, and TNn are a measure of hot and cold extreme temperature events, whereas the number of frost days and tropical nights are examples of threshold-based indices that may be relevant for impact assessment studies.
by several factors which may depend on the type of per- turbation applied to the climate system, and on the physical parameterizations of atmospheric models. These factors include the change in large-scale vertical velocity, the change in the vertical stratification of the tropical atmo- sphere above the free troposphere (which is partly con- trolled by remote deep convective processes), and the change in the moistening of the free troposphere by shal- low cumulus convection. This latter process is likely to be particularly critical since it may partly oppose the robust effect of the Clausius-Clapeyron relationship on the MSE vertical gradient. Combined with the ubiquitous occurrence of shallow cumulus clouds over tropical oceans, it suggests (as already emphasized by earlier studies such as Bony et al. 2004 ; Medeiros et al. 2008 ) that the representation of shallow cumulus convection by climate models and its response to globalwarming is particularly critical for cli- mate sensitivity and should be thoroughly tested. The rel- ative magnitude of changes in MSE vertical advection versus surface fluxes or radiative cooling may also depend on the representation of the mean present-day climate by GCMs. Depending on how the different climate models simulate the present climate, the change in large-scale atmospheric circulation and the vertical stratification of the tropical atmosphere, a given external perturbation may thus lead to different low-level cloud responses. This presum- ably explains the wide range of low-level cloud responses predicted by climate models underclimate change (e.g. Bony and Dufresne 2005 ; Webb et al. 2006 ; Medeiros et al. 2008 ), and thus the large uncertainty in climatesensitivity.
The scenario assumes that all existing policies (such as energy prices and investment) remain in place as well as established link between temperature change and agricultural productivity. Yet, another scenario removes agricultural damages and thereby providing a measure of how important these might be on a regional scale. Again, the mitigation scenario assumes full participation and an efficient mechanism for reducing emissions through a globally applied uniform tax on carbon emissions. Here, all tax revenues are recycled internally and there is no cap or trade system that could lead to a re-allocation of tax revenues across countries. Regrettably, the base line scenario leads to a carbon concentration that rises from around 390 parts per million (PPM) in 2001 to 560ppm in 2050. Clearly, this was well above any stabilization scenario of 450ppm promoted by some as an upper limit to avoid severe damages or the more modest target of 550ppm that many others perceive as a threshold not to surpass. As worrisome as the overall concentration level in 2050, the observed path was far from a stabilization scenario with concentrations likely to continue increasing well beyond 2050. In fact, the true objective was the overall rise in temperature that is driven by an increase in irradiative forcing given climatesensitivity rises to 1.75 o C relative to 1900 levels (IPCC, 2007).
3. Linkage Between the Meridional Structures of the Hadley Circulation Change and Cloud Change
The multimodel-mean zonal-mean vertical pressure velocity ( ω) changes from the 15 models are shown in Figure 1a, with the climatological ω proﬁles superimposed as contours. Only the latitudes from 45°S to 40°N within the Hadley Circulation boundaries are shown. It is true that the signs of the ω change oppose those of the climatological ω over many latitudinal zones, corresponding to a weakening of the circulation. However, there are latitudinal bands where the ω changes are of the same sign as the climatological ω, including the equatorial region (~5°S to ~5°N) and the ﬂanks of the descent zones (~30°N to ~40°N, ~15°S to ~20°S, and ~30°S to ~45°S), corresponding to a strengthening of the circulation. The latitudinally alternating positive and negative ω changes are also indicative of the shifts of the ascent and descent boundaries. In the upper troposphere and within the climatological ascending branch, a uniform increase of the upward motion marks the deepening of the troposphere and the upward expansion of the Hadley Circulation. All of these features underscore the complex structures in the response of the Hadley Circulation underglobalwarming. We attempt to establish the linkage between the structures in the Hadley Circulation change and the clouds and TOA CRE changes in the RCP 4.5 scenario, as shown in Figures 1b and 1c, with particular attention to the underappreciated strengthening segments in the Hadley Circulation change. Zonal-mean relative humidity change is superimposed on the cloud fraction change in Figure 1b. Cloud radiative effect (CRE) is de ﬁned as the all-sky and clear-sky radiative ﬂux differences with a positive sign representing a warming effect to the Earth-atmosphere system. In Figure 1c, a positive CRE change corresponds to an increase of cloud warming effect or a decrease of cloud cooling effect.
(Manuscript received 18 April 2018, in final form 7 September 2018) ABSTRACT
How the globally uniform component of sea surface temperature (SST) warming influences rainfall in the African Sahel remains insufficiently studied, despite mean SST warming being among the most robustly simulated and theoretically grounded features of anthropogenic climate change. A prior study using the NOAA Geophysical Fluid Dynamics Laboratory (GFDL) AM2.1 atmospheric general circulation model (AGCM) demonstrated that uniform SST warming strengthens the prevailing northerly advection of dry Saharan air into the Sahel. The present study uses uniform SST warming simulations performed with 7 GFDL and 10 CMIP5 AGCMs to assess the robustness of this drying mechanism across models and uses observations to assess the physical credibility of the severe drying response in AM2.1. In all 17 AGCMs, mean SST warming enhances the free-tropospheric meridional moisture gradient spanning the Sahel and with it the Saharan dry-air advection. Energetically, this is partially balanced by anomalous subsidence, yielding decreased precipitation in 14 of the 17 models. Anomalous subsidence and precipitation are tightly linked across the GFDL models but not the CMIP5 models, precluding the use of this relationship as the start of a causal chain ending in an emergent observational constraint. For AM2.1, cloud–rainfall co- variances generate radiative feedbacks on drying through the subsidence mechanism and through surface hydrology that are excessive compared to observations at the interannual time scale. These feedbacks also act in the equilibrium response to uniform warming, calling into question the Sahel’s severe drying response to warming in all coupled models using AM2.1.
The POP simulation was initialized from a 75-year spin- up simulation (Maltrud et al., 2008) under the CORE-I cli- matology dataset (Large and Yeager, 2004) as atmospheric forcing. This initial condition is indicated here as the year 1950. Under a freshwater flux which is diagnosed from the last 5 years of this spin-up, the model displays only a very small drift over a 200-year control simulation (Le Bars et al., 2016). Here, the model was forced with monthly mean atmospheric forcing fluxes over the period 1950–2100, which were derived from simulations with the ECHAM5- OM1 model within the ESSENCE (Sterl et al., 2008) project (see www.knmi.nl/~sterl/Essence/). The used forcing fields are 10 m wind speed, downward flux of short-wave and longwave radiation, 2 m temperature, humidity, precipitation, runoff, and the surface wind stress field. The atmospheric forcing fields are given on a global 1 ◦ × 1 ◦ grid and are inter- polated to the curvilinear POP model grid. The outgoing heat and freshwater fluxes are computed within the model using bulk formulae. There is an initial adjustment after the switch in forcing in 1950, for example measured by the change in the AMOC strength, which lasts for about a decade.
only positive trends occur, while the other variables display a mixture of positive and negative trends (see Figs. 3–6). This implies the existence of transition zones between ar- eas with positive and negative trends in the monthly fields where trends are de facto zero and therefore no significant slopes can be found. In addition, cloudiness and precipita- tion both exhibit strong interannual variability that tends to mask weak trends that primarily occur around such transi- tion zones. Similarly, the estimation of parameters of the lo- gistic regression model for change of rain month frequency is hampered by the stochastic nature of this variable. More- over, vast areas with a rain month frequency of 100 % (e.g. in the high latitudes and the wet tropics) remain unaffected by the occurrence of dry months underclimate change (Fig. 6). For each derived anomaly pattern two statistics – mean and standard deviation – are calculated in order to characterise the patterns. We took into account the spatial and temporal coverage of the individual slopes – i.e. by weighting them with the respective cell area and length of month. Because the aim is to illustrate the properties of the entire pattern as it is applied, grid cells and months without a significant slope are included as zero values.
Tourism is a very important branch of the economy, but it is important also for human entertainment, relaxation, and recreation. Without doubt, climate is one of the essential parameters influencing tourism. Changes in globalclimate are beyond the control of the tourism industry and may have far-reaching consequences for many current tourist destinations as well as for places contemplating involvement in tourism. Understanding how climate and weather influence tourism is necessary if we want to estimate the impacts of climate change on tourism.
Globalwarming and climate change, a multidisciplinary topic is a matter of international concern. There are some international environmental treaties related to globalwarming and climate change. The most significant international agreement in this area is - UNFCCC - the United Nations Framework Convention on Climate Change adopted at the Rio Earth Summit in 1992 and ratified by 195 countries. It mainly deals with greenhouse gases emissions mitigation, adaptation and finance starting in the year 2020. The Kyoto Protocol which extends the 1992 UNFCC mandates State Parties to reduce greenhouse gas emissions: its two basic premises are - globalwarming exists, and human-made CO2 emissions caused globalwarming. The Kyoto Protocol came into force in 2005 and each COP has served as the ‘meeting of parties’ to Kyoto Protocol such as COP13 (Bali, 2007), COP15 (Copenhagen, 2009), COP16 (Cancun, 2010), COP17 (Durban, 2011), COP18 (Doha, 2012), COP19 (Warsaw, 2013), COP21 and (Paris, 2015).There are other international legal instruments such as 1979 Geneva Convention on Long-Range Trans-boundary Air Pollution. Against this backdrop, this paper will critically examine the existing international legal regime (treaties, conventions, agreements, etc.) on globalwarming and climate change.
150 years of industrialization have rendered the future of the Mother Nature at stake which will drastically change the equation of coming generations. It is predicted that the average temperature of the atmosphere will raise minimum by 10 degrees in the next century. Kyoto protocol has encouraged innovators and inventors to streamline their R&D for technologies that reduce greenhouse gas emissions. Countries have to accelerate research in alternate forms of energy. There must be international cooperation taking into consideration the legitimate requirement of developing and least developing countries to develop without compromising their responsibility for sustainable development. Then only the ultimate objective of UNFCCC- WR DFKLHYH D OHYHO ´ZLWKLQ D WLPH- frame sufficient to allow ecosystems to adapt naturally to climate change, to ensure that food production is not threatened and to enable economic development to proceed in a VXVWDLQDEOH PDQQHUµ FDQ EH DFKLHYHG The other two sisters Rio Earth Summit Conventions are (i) the UN Convention on Biological Diversity and (ii) the Convention to Combat Desertification. All the three conventions aim to encourage mutual cooperation for developing synergies in their activities. Now it incorporates Ramsar Convention on Wetlands as well.
Also in these calculations, the increase in global mean pre- cipitation is in the range of 1% per DGW to 2 % per DGW except for the GFDL experiment, which shows a very small increase (indicating that in this model most of the precipi- tation increase occurs in the polar regions). In all cases ex- cept for MIROC the increase in global SDII is greater than the increase in mean precipitation, resulting in a decrease in the number of rainy days. The changes in the 95th, 99th and 99.9th percentiles are maximum for the most extreme percentiles, showing that the main contribution to the HIRF response is due to the highest-intensity events, i.e., above the 99th and 99.9th percentiles, whose response becomes in- creasingly closer to the Cl–Cl one (and even super-Cl–Cl for the GFDL model). In fact, the increase in the 95th percentile for the ensemble model average is lower than the increase in SDII, and this is because in some models the threshold inten- sity in Figs. 3–6, for which the sign of the change turns from negative to positive, lies beyond the 95th percentile. When only land areas between 60 ◦ S and 60 ◦ N are taken into ac- count (bottom panel in Table 1), the changes are generally in line with the global ones, except for the CNRM model. Over land areas we also find changes in the highest percentiles of magnitude mostly greater than over the globe (and thus over oceans).
A number of global coupled climate models, including three developed at the Geophysical Fluid Dynamics Labora- tory (GFDL) have included a tracer of ventilation age known as the ideal age. This tracer is set to zero in the mixed layer and ages at a rate of 1 yr/yr thereafter. (Bryan et al., 2006) re- cently presented simulations using ideal age in the National Center for Atmospheric Research’s coupled model. In this paper, we examine simulations using this tracer and find that one of the most significant changes in the ideal age field un- der globalwarming is that a number of regions where oxy- gen is currently at low levels become younger. The reasons for such a surprising result are examined in one model for which full term balances are available and are attributed to a decrease a change in the balance of waters supplying these regions, with a generally decreasing contribution from older deep and intermediate waters. Implications of this result for global biogeochemical cycles are considered.
This analysis encompasses Oregon (OR) and Washington (WA) in the northwestern United States (US) with a pop- ulation of nearly 10.5 million (US Census Bureau, 2010). The elevation varies from sea level to over 4300 m at Mount Rainier, with the north–south trending mountains of the Cas- cade Range dividing the western and eastern portions of the states (Fig. 1a). The study region is divided into 13 phys- iographic sections (Fig. 1b) based on common topography, rock type, structure, and geomorphic history (Fenneman and Johnson, 1946). The maritime climate is highly influenced by the Pacific Ocean and varies with elevation and distance from the coast (Fig. 1b and c). Long-term average precipita- tion ranges from 150 mm in the Columbia Valley on the east side of the Cascades to ∼ 7000 mm in the Olympic Moun- tains (Daly et al., 2008, Fig. 1c). Both OR and WA have extreme wet (winter) and dry (summer) seasons, but the sea- sonal distribution of precipitation varies between the region’s eastern and western halves. While most of the annual preci- pitation occurs during fall and winter, more frequent summer thunderstorms in the eastern half result in a slightly higher summer precipitation (Mass, 2008). An altitudinal tempera- ture gradient, varying by latitude (Fig. 1c), controls the phase of precipitation with winter rain (R) in lower elevations, sea- sonal snow at higher elevations (SSZ), and transient snow at intermediate elevations (TSZ) (Jefferson, 2011). The ma- jority of the winter precipitation occurs as rain in the Coast Range and as snow along the Cascades and other ranges (e.g., Wallowa and Blue Mountains).
DOI: 10.4236/vp.2018.44005 52 Voice of the Publisher lin immediately saw new possibilities and a strong argument for leaving oil de- pendence and rapidly building up our nuclear power industry (“ if we in 1990 do not have at least 24 nuclear power plants , it will not survive as an industrial na- tion ”). Already in 1975, the fear of a future increase in atmospheric CO 2 content