Increase In Global Mean Surface Temperature Research Articles

Changes in Classified Precipitation in Iran

Changes in classified precipitation in Iran and its relationship with global mean surface temperature (GMST) have not been comprehensively investigated. Therefore, this study analyzed changes in precipitation of different intensities over Iran for the 1987–2017 period. Results show that the total annual precipitation (PRCPTOT) and the number of wet days (RR) have significantly decreased over Iran. Also, the mean precipitation intensity (SDII) has increased somewhat. There is a non-uniform change for three intensity categories of precipitation. The amounts (frequency) of light, moderate, and heavy precipitation have significantly decreased at 47% (57.9%), 18.7% (15.8%), and 3.94% (7.9%) of stations respectively. Therefore, the decrease in the amount and frequency of light and moderate precipitation is more severe than heavy precipitation and the proportion of heavy precipitation to the total annual precipitation has increased somewhat during 1987-2017. Overall, the result shows that the intensity of decreasing trends of amount and frequency of precipitation has increased from the south (east) to the north (west) of Iran. Also, SDII has increased from the south (east) to the north (west) of Iran. The sensitivity value was obtained by calculating the ratio of linear trends of precipitation indices and GMST. The regional median sensitivity and percentage change in PRCPTOT, RR, and SDII per 1-kelvin increase in GMST are -6.1%, -11.2%, and 12.9% respectively. Considering that Iran is located in the arid subtropical region, a significant decrease in the amount and frequency of precipitation may have destructive effects on water resources.

Understanding Future Increases in Precipitation Extremes in Global Land Monsoon Regions

AbstractThis study investigates future changes in daily precipitation extremes and the involved physics over the global land monsoon (GM) region using climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The daily precipitation extreme is identified by the cutoff scale, measuring the extreme tail of the precipitation distribution. Compared to the historical period, multimodel results reveal a continuous increase in precipitation extremes under four scenarios, with a progressively higher fraction of precipitation exceeding the historical cutoff scale when moving into the future. The rise of the cutoff scale by the end of the century is reduced by 57.8% in the moderate emission scenario relative to the highest scenario, underscoring the social benefit in reducing emissions. The cutoff scale sensitivity, defined by the increasing rates of the cutoff scale over the GM region to the global mean surface temperature increase, is nearly independent of the projected periods and emission scenarios, roughly 8.0% K−1by averaging all periods and scenarios. To understand the cause of the changes, we applied a physical scaling diagnostic to decompose them into thermodynamic and dynamic contributions. We find that thermodynamics and dynamics have comparable contributions to the intensified precipitation extremes in the GM region. Changes in thermodynamic scaling contribute to a spatially uniform increase pattern, while changes in dynamic scaling dominate the regional differences in the increased precipitation extremes. Furthermore, the large intermodel spread of the projection is primarily attributed to variations of dynamic scaling among models.

Estimating a social cost of carbon for global energy consumption.

Estimates of global economic damage caused by carbon dioxide (CO2) emissions can inform climate policy1-3. The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO2 emissions today impact future economic outcomes through altering the climate4-6. Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC7,8, but they rely on models9-11 that are spatially coarse and not tightly linked to data2,3,6,7,12,13. Here we show that the release of one ton of CO2 today is projected to reduce total future energy expenditures, with most estimates valued between -US$3 and -US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annualglobal electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption)per one-degree-Celsius increase in global mean surfacetemperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsiusincrease in GMST. Our finding of net savings contradicts previous research7,8, because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight14.

Double benefit of limiting global warming for tropical cyclone exposure

Tropical cyclone (TC) impacts are expected to worsen under continued global warming and socio-economic development. Here we combine TC simulations with an impact model to quantify country-level population exposure to TC winds for different magnitudes of global mean surface temperature increase and future population distributions. We estimate an annual global TC exposure increase of 26% (33 million people) for a 1 °C increase in global mean surface temperature, assuming present-day population. The timing of warming matters when additionally accounting for population change, with global population projected to peak around mid-century and decline thereafter. A middle-of-the-road socio-economic scenario combined with 2 °C of warming around 2050 increases exposure by 41% (52 million). A stronger mitigation scenario reaching 2 °C around 2100 limits this increase to 20% (25 million). Rapid climate action therefore avoids interference with peak global population timing and limits climate-change-driven exposure. Cumulatively, over 1.8 billion people could be saved by 2100.

Projections of South Asian Summer Monsoon Under Global Warming from 1.5° to 5°C

AbstractThe South Asian summer monsoon (SASM) is one of the most crucial climate components in boreal summer. The future potential changes in the SASM have great importance for climate change adaption and policy setting in this populous region. To understand the SASM changes and its link with the global warming of 1.5°C to 5°C above the preindustrial level, we investigate the changes in the SASM circulation and precipitation based on a large-ensemble simulation conducted with Canadian Earth System Model version 2 (CanESM2). With the global mean surface temperature (GMST) increase, the large-ensemble mean of SASM circulation is projected to weaken almost linearly while the precipitation and precipitable water to enhance quasi-linearly. A double anticyclone along the tropical Indian Ocean is a major anomalous circulation pattern for each additional degree of warming and is responsible for the weakening of the lower-level westerlies. The decreased upper-level land-sea thermal contrast (TCupper) is the main thermal driver for the weakening of the SASM circulation while the lower-level thermal contrast contributes little. The non-linearly decreased TCupper is mainly related to the temperature response to the increased CO2 forcing and convection induced latent heat release in the tropics. The increase in the SASM precipitation is mainly due to the quasi-linearly increased positive contribution of the thermodynamic component, while the dynamic component has a negative impact. Both horizontal moisture advection and moisture convergence contribute to the precipitation increase, and moisture convergence plays a dominant role. These results provide new insight that the SASM changes can be roughly scaled by the GMST changes.

Nighttime warming alleviates the incidence of juniper forest growth decline on the Tibetan Plateau

Recent warming over the Tibetan Plateau (TP) is approximately twice the global-mean surface temperature increase and poses a threat to the healthy growth of forests. Although many studies have focused on whether recent climate warming has caused forest growth decline on the TP, it remains unclear how asymmetric warming, that is faster increasing nighttime temperature than daytime, impacts forest growth decline. We explored this question by using a ring-width index series from 1489 juniper trees (Juniperus prezwalskii and J. tibetica) at 50 sites on the TP. We calculated the percentage of trees with growth decline (PTD) to reconstruct historical forest growth decline and employed a piecewise structural equation meta-model (pSEM) and linear mixed model (LMM) to explore influencing factors. We found that the PTD has decreased since the late 19th century, with an abrupt decreasing trend since the 1980s. Results of the pSEM show that winter minimum temperature has a stronger indirect negative effect on the variation in PTD (β = −0.24, p < 0.05) compared to that of the weak indirect positive effect of summer maximum temperature (β = 0.16, p < 0.05). The results of LMM show that the variation in PTD is directly negatively (p < 0.001) affected by both winter minimum temperature and summer total precipitation, but the former has a greater independent contribution than the latter (with 17.7% vs 2.5% of variances independently explained, respectively). These results suggest that increased winter minimum temperature substantially mitigates the growth decline in juniper forests on the TP. As the minimum temperature generally occurs at night, we conclude that the asymmetric increase in nighttime temperature has decreased the incidence of juniper forest growth decline on the TP under climate warming. This alleviating effect of nighttime warming is likely due to reduced low-temperature constraints and reduced damage to tree growth.

A Global, Continental, and Regional Analysis of Changes in Extreme Precipitation

AbstractThis paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.

Surface warming patterns dominate the uncertainty in global water vapor plus lapse rate feedback

Climate feedbacks have been usually estimated using changes in radiative effects associated with increased global-mean surface temperature. Feedback uncertainties, however, are not only functions of global-mean surface temperature increase. In projections by global climate models, it has been demonstrated that the geographical variation of sea surface temperature change brings significant uncertainties into atmospheric circulation and precipitation responses at regional scales. Here we show that the spatial pattern of surface warming is a major contributor to uncertainty in the combined water vapour-lapse rate feedback. This is demonstrated by computing the global-mean radiative effects of changes in air temperature and relative humidity simulated by 31 climate models using a methodology based on radiative kernels. Our results highlight the important contribution of regional climate change to the uncertainty in climate feedbacks, and identify the regions of the world where constraining surface warming patterns would be most effective for higher skill of climate projections.

Potential impact assessment for China's dry and wet areas under global warming targets

Since the Industrial Revolution, greenhouse gas emissions have caused global temperatures to rise gradually. The Paris Agreement aims to limit the global mean surface temperature increase to less than 2.0 °C relative to pre-industrial levels. China's land area accounts for ∼7% of the global land (65Ñ59S) area, and the arid area of China accounts for 12.41% of the total global arid area. Therefore, the degree of aridification/humidification in China is of great significance to climate change in China and the world. This paper evaluates the regional changes of drylands and humid lands in China based on the 1.5 °C and 2.0 °C increase targets for global mean surface warming(GMSW). We find a suitable warming target for China's climate from the perspective of aridification. The results show the following: 1) If the global mean surface temperature rises by 1.5 °C and 2.0 °C above pre-industrial levels, the mean surface temperature in China will increase by 2.0 °C and 2.8 °C, respectively. 2) Under the two GMSW targets, the degree of drought in some extremely arid areas in China was projected to decrease, which means that these areas may gradually be suitable for human habitation. 3) Regardless of which global surface temperature target is maintained, the humid lands in China will bear greater risks from warming than the drylands. Under these circumstances, the 1.5 °Ctargetwill lead to better outcomes for China than the 2.0 °C target.

Signal detection in global mean temperatures after “Paris”: an uncertainty and sensitivity analysis

Abstract. In December 2015, 195 countries agreed in Paris to “hold the increase in global mean surface temperature (GMST) well below 2.0 ∘C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 ∘C”. Since large financial flows will be needed to keep GMSTs below these targets, it is important to know how GMST has progressed since pre-industrial times. However, the Paris Agreement is not conclusive as regards methods to calculate it. Should trend progression be deduced from GCM simulations or from instrumental records by (statistical) trend methods? Which simulations or GMST datasets should be chosen, and which trend models? What is “pre-industrial” and, finally, are the Paris targets formulated for total warming, originating from both natural and anthropogenic forcing, or do they refer to anthropogenic warming only? To find answers to these questions we performed an uncertainty and sensitivity analysis where datasets and model choices have been varied. For all cases we evaluated trend progression along with uncertainty information. To do so, we analysed four trend approaches and applied these to the five leading observational GMST products. We find GMST progression to be largely independent of various trend model approaches. However, GMST progression is significantly influenced by the choice of GMST datasets. Uncertainties due to natural variability are largest in size. As a parallel path, we calculated GMST progression from an ensemble of 42 GCM simulations. Mean progression derived from GCM-based GMSTs appears to lie in the range of trend–dataset combinations. A difference between both approaches appears to be the width of uncertainty bands: GCM simulations show a much wider spread. Finally, we discuss various choices for pre-industrial baselines and the role of warming definitions. Based on these findings we propose an estimate for signal progression in GMSTs since pre-industrial.

Slow ocean response and the 1.5 and 2&amp;deg;C warming targets

Recent studies reveal that to achieve the 1.5 or 2°C warming target proposed in the 2015 Paris Agreement, greenhouse gas (GHGs) concentration is required to decrease after reaching a peak around the middle of this century, distinct from most scenarios without configuring GHGs concentration decrease. Climate response to external radiative forcing under two different scenarios is then investigated in this study by simulations from 8 models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Based on an idealized two-box (ocean mixed layer and the deeper layer) model, global-mean surface temperature (GMST) change is further divided into components due to the fast response of the ocean mixed-layer (fast contribution) and the deeper ocean slow evolution (slow contribution), respectively, to explain the GMST trajectory in CMIP5 simulations. The response timescale is 3–5 a for the fast contribution and decades to centuries for the slow contribution. Under the scenario with radiative forcing (RF) firstly increases and then levels off towards a constant after 2070 (RCP4.5), GMST rises rapidly at the first stage and slowly after RF levels off. In comparison, under the scenario with RF firstly increases and then decreases after 2045 (RCP2.6), GMST rises rapidly to a peak below 2°C and then declines at a very slow rate, following a nearly flat trajectory between 2050 and 2100. Corresponding to the GMST change, the deeper ocean warms faster than the upper ocean after the RF levels off in RCP4.5 while decreasing in RCP2.6. In fact, the GMST trajectory depends on the ratio between the fast and slow contributions from the ocean at different stages under different RF pathways. During the RF increase period, GMST mainly follows the RF pathway because the fast contribution dominates. After RF holds constant or decreases, slow contribution due to the deeper ocean warming increases persistently, causing the GMST change to deviate from the RF pathway when RF holds constant or decreases. As a result, contribution from the deeper ocean slow warming to the magnitude and trajectory of GMST change cannot be neglected in scenarios for the 1.5 and 2°C low warming target. For RCP2.6, GMST increase is 1.83°C in 2100, the time when RF is in decrease. In contrast, for RCP4.5, GMST reaches the same increase in 2033, the time when RF is still increasing. Despite the same GMST increase, climate responses in 2033 under RCP4.5 are distinct from that in 2100 under RCP2.6. For example, ocean stratification weakens in 50–300 m in 2100 under RCP2.6 but strengthens at all depths in 2033 under RCP4.5 due to the persistent heat accumulation in the deeper ocean. Global-mean thermosteric sea-level rise due to thermal expansion of seawater warming is much higher in RCP2.6 (16.39 cm) than that in RCP4.5 (9.01 cm). Moreover, surface warming pattern displays substantial structural differences between RCP2.6 and RCP4.5, especially over regions with strong ocean dynamics and hence large deeper ocean feedback to surface warming. Specifically, surface warming is notably larger over the North Pacific Ocean and Southern Ocean in 2100 under RCP2.6 than that in 2033 under RCP4.5, while the opposite is true over the subtropical and mid-latitude land regions in the Northern Hemisphere. Under most CMIP5 scenarios without a RF decrease, the 1.5 or 2°C GMST warming target is projected to reach much earlier than 2100, which are used in some studies to represent the climate responses for the Paris targets at the end of this century. These results underestimate the effect of the deeper ocean slow contribution and are biased in projecting climate response for the Paris targets.

What Caused the Global Surface Warming Hiatus of 1998–2013?

Research into the mechanisms for the global warming slowdown or “hiatus” of 1998–2013 is reviewed here. Observational and modeling studies identify tropical Pacific sea surface temperature variability as a major pacemaker of global mean surface temperature (GMST) change, as corroborated by the GMST increase following a major El Nino event. Specifically, the decadal cooling of the tropical Pacific contributes to the recent global warming hiatus. This tropical Pacific pacemaker effect appears larger for decadal than interannual variability, but the decadal effect remains to be quantified from observations. Our critical review of the literature reveals that the internal and radiatively forced GMST changes are distinct in pattern, energetics, mechanism, and predictability. In contrast to greenhouse gas-induced warming that is spatially uniform in sign and driven by energy perturbations, internal variability in GMST is an order of magnitude smaller than spatial variations, for which ocean-atmosphere interaction is of first-order importance while planetary energetics is not. In fact, decadal variability in GMST is poorly correlated with net radiation at the top of the atmosphere, highlighting the need to distinguish internal and forced GMST change in planetary energy budget. While the planetary energy budget can now be closed observationally over multi-decadal periods, the recent hiatus highlights the need and challenges to measure and quantify decadal changes in both global ocean heat uptake (e.g., for the effect of radiative forcing on the hiatus) and heat redistribution in the ocean. Hiatus research has led to a wide recognition of the importance of internal variability for GMST trends over a decade and longer. The strengthened connection between the climate variability and change communities is an important legacy of hiatus research.

Time-Varying Response of ENSO-Induced Tropical Pacific Rainfall to Global Warming in CMIP5 Models. Part II: Intermodel Uncertainty

Anomalous rainfall in the tropical Pacific driven by El Niño–Southern Oscillation (ENSO) is a crucial pathway of ENSO’s global impacts. The changes in ENSO rainfall under global warming vary among the models, even though previous studies have shown that many models project that ENSO rainfall will likely intensify and shift eastward in response to global warming. The present study evaluates the robustness of the changes in ENSO rainfall in 32 CMIP5 models forced under the representative concentration pathway 8.5 (RCP8.5) scenario. The robust increase in mean-state moisture dominates the robust intensification of ENSO rainfall. The uncertain amplitude changes in ENSO-related SST variability are the largest source of the uncertainty in ENSO rainfall changes through influencing the amplitude changes in ENSO-driven circulation variability, whereas the structural changes in ENSO SST and ENSO circulation enhancement in the central Pacific are more robust than the amplitude changes. The spatial pattern of the mean-state SST changes—the departure of local SST changes from the tropical mean—with an El Niño–like pattern is a relatively robust factor, although it also contains pronounced intermodel differences. The intermodel spread of historical ENSO circulation is another noteworthy source of the uncertainty in ENSO rainfall changes. The intermodel standard deviation of ENSO rainfall changes increases along with the increase in global-mean surface temperature. However, the robustness of enhanced ENSO rainfall changes in the central-eastern Pacific is almost unchanged, whereas the eastward shift of ENSO rainfall is increasingly robust along with the increase in global-mean surface temperature.

Nuclear energy and bio energy carbon capture and storage, keys for obtaining 1.5°C mean surface temperature limit

A rapid development of nuclear energy production reaching 173 EJ/y in 2060 and 605 EJ/y in 2110 limits the Global Mean Surface Temperature (GMST) increase to 1.5°C with respect to preindustrial value, with a reduction of the stored carbon dioxide from 800 Gt in the original MESSAGE-Efficiency scenario to 275 Gt in the present one, while multiplying by 6 the Total Primary Energy Supply between 2015 and 2110.

The contribution of greenhouse gases to the recent slowdown in global-mean temperature trends

The recent slowdown in the rate of increase in global-mean surface temperature (GMST) has generated extensive discussion, but little attention has been given to the contribution of time-varying trends in greenhouse gas concentrations. We use a simple model approach to quantify this contribution. Between 1985 and 2003, greenhouse gases (including well-mixed greenhouse gases, tropospheric and stratospheric ozone, and stratospheric water vapour from methane oxidation) caused a reduction in GMST trend of around 0.03–0.05 K decade−1 which is around 18%–25% of the observed trend over that period. The main contributors to this reduction are the rapid change in the growth rates of ozone-depleting gases (with this contribution slightly opposed by stratospheric ozone depletion itself) and the weakening in growth rates of methane and tropospheric ozone radiative forcing. Although CO2 is the dominant greenhouse gas contributor to GMST trends, the continued increase in CO2 concentrations offsets only about 30% of the simulated trend reduction due to these other contributors. These results emphasize that trends in non-CO2 greenhouse gas concentrations can make significant positive and negative contributions to changes in the rate of warming, and that they need to be considered more closely in analyses of the causes of such variations.

A review of progress towards understanding the transient global mean surface temperature response to radiative perturbation

The correct understanding of the transient response to external radiative perturbation is important for the interpretation of observed climate change, the prediction of near-future climate change, and committed warming under climate stabilization scenarios, as well as the estimation of equilibrium climate sensitivity based on observation data. It has been known for some time that the radiative damping rate per unit of global mean surface temperature increase varies with time, and this inconstancy affects the transient response. Knowledge of the equilibrium response alone is insufficient, but understanding the transient response of the global mean surface temperature has made rapid progress. The recent progress accompanies the relatively new concept of the efficacies of ocean heat uptake and forcing. The ocean heat uptake efficacy associates the temperature response induced by ocean heat uptake with equilibrium temperature response, and the efficacy of forcing compares the temperature response caused by non-CO2 forcing with that by CO2 forcing. In this review article, recent studies on these efficacies are discussed, starting from the classical global feedback framework and basis of the transient response. An attempt is made to structure different studies that emphasize different aspects of the transient response and to stress the relevance of those individual studies. The implications on the definition and computation of forcing and on the estimation of the equilibrium response in climate models are also discussed. Along with these discussions, examples are provided with MIROC climate model multi-millennial simulations.

Disentangling greenhouse warming and aerosol cooling to reveal Earth’s climate sensitivity

Earth’s climate sensitivity has been debated. An econometric analysis of observations shows that aerosol cooling has masked about one-third of greenhouse gas warming and yields a transient climate sensitivity of 2 ± 0.8 °C. Earth’s climate sensitivity has long been subject to heated debate and has spurred renewed interest after the latest IPCC assessment report suggested a downward adjustment of its most likely range1. Recent observational studies have produced estimates of transient climate sensitivity, that is, the global mean surface temperature increase at the time of CO2 doubling, as low as 1.3 K (refs 2,3), well below the best estimate produced by global climate models (1.8 K). Here, we present an observation-based study of the time period 1964 to 2010, which does not rely on climate models. The method incorporates observations of greenhouse gas concentrations, temperature and radiation from approximately 1,300 surface sites into an energy balance framework. Statistical methods commonly applied to economic time series are then used to decompose observed temperature trends into components attributable to changes in greenhouse gas concentrations and surface radiation. We find that surface radiation trends, which have been largely explained by changes in atmospheric aerosol loading, caused a cooling that masked approximately one-third of the continental warming due to increasing greenhouse gas concentrations over the past half-century. In consequence, the method yields a higher transient climate sensitivity (2.0 ± 0.8 K) than other observational studies.

Simulated responses of terrestrial aridity to black carbon and sulfate aerosols

AbstractAridity index (AI), defined as the ratio of precipitation to potential evapotranspiration (PET), is a measure of the dryness of terrestrial climate. Global climate models generally project future decreases of AI (drying) associated with global warming scenarios driven by increasing greenhouse gas and declining aerosols. Given their different effects in the climate system, scattering and absorbing aerosols may affect AI differently. Here we explore the terrestrial aridity responses to anthropogenic black carbon (BC) and sulfate (SO4) aerosols with Community Earth System Model simulations. Positive BC radiative forcing decreases precipitation averaged over global land at a rate of 0.9%/°C of global mean surface temperature increase (moderate drying), while BC radiative forcing increases PET by 1.0%/°C (also drying). BC leads to a global decrease of 1.9%/°C in AI (drying). SO4 forcing is negative and causes precipitation a decrease at a rate of 6.7%/°C cooling (strong drying). PET also decreases in response to SO4 aerosol cooling by 6.3%/°C cooling (contributing to moistening). Thus, SO4 cooling leads to a small decrease in AI (drying) by 0.4%/°C cooling. Despite the opposite effects on global mean temperature, BC and SO4 both contribute to the twentieth century drying (AI decrease). Sensitivity test indicates that surface temperature and surface available energy changes dominate BC‐ and SO4‐induced PET changes.

Does extreme precipitation intensity depend on the emissions scenario?

The rate of increase of global-mean precipitation per degree global-mean surface temperature increase differs for greenhouse gas and aerosol forcings and across emissions scenarios with differing composition of change in forcing. We investigate whether or not the rate of change of extreme precipitation also varies across the four emissions scenarios that force the CMIP5 multi-model ensemble. In most models, the rate of increase of maximum annual daily precipitation per degree global warming in the multi-model ensemble is statistically indistinguishable across the four scenarios, whether this extreme precipitation is calculated globally, over all land, or over extra-tropical land. These results indicate that, in contrast to mean precipitation, extreme precipitation depends on the total amount of warming and does not depend on emissions scenario in most models.

Forcing, feedback and internal variability in global temperature trends.

Most present-generation climate models simulate an increase in global-mean surface temperature (GMST) since 1998, whereas observations suggest a warming hiatus. It is unclear to what extent this mismatch is caused by incorrect model forcing, by incorrect model response to forcing or by random factors. Here we analyse simulations and observations of GMST from 1900 to 2012, and show that the distribution of simulated 15-year trends shows no systematic bias against the observations. Using a multiple regression approach that is physically motivated by surface energy balance, we isolate the impact of radiative forcing, climate feedback and ocean heat uptake on GMST--with the regression residual interpreted as internal variability--and assess all possible 15- and 62-year trends. The differences between simulated and observed trends are dominated by random internal variability over the shorter timescale and by variations in the radiative forcings used to drive models over the longer timescale. For either trend length, spread in simulated climate feedback leaves no traceable imprint on GMST trends or, consequently, on the difference between simulations and observations. The claim that climate models systematically overestimate the response to radiative forcing from increasing greenhouse gas concentrations therefore seems to be unfounded.