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Continental runoff has increased through the twentieth century despite more intensive human water consumption. Possible reasons for the increase include: climate change and variability, deforestation, solar dimming, and direct atmospheric carbon dioxide (CO2) effects on plant transpiration. All of these mechanisms have the potential to affect precipitation and/or evaporation and thereby modify runoff. Here we use a mechanistic land-surface model and optimal fingerprinting statistical techniques to attribute observational runoff changes into contributions due to these factors. The model successfully captures the climate-driven inter-annual runoff variability, but twentieth-century climate alone is insufficient to explain the runoff trends. Instead we find that the trends are consistent with a suppression of plant transpiration due to CO2-induced stomatal closure. This result will affect projections of freshwater availability, and also represents the detection of a direct CO2 effect on the functioning of the terrestrial biosphere.

Changes in rainfall affect drinking water, river and surface runoff, soil moisture, groundwater reserve, electricity generation, agriculture production and ultimately the economy of a country. Trends in rainfall, therefore, are important for examining the impact of climate change on water resources for its planning and management. Here, as analysed from 119 years of rainfall measurements at 16 different rain gauge stations across northeast India, a significant change in the rainfall pattern is evident after the year 1973, with a decreasing trend in rainfall of about 0.42 ± 0.024 mm dec−1. The wettest place of the world has shifted from Cherrapunji (CHE) to Mawsynram (MAW) (separated by 15 km) in recent decades, consistent with long-term rainfall changes in the region. The annual mean accumulated rainfall was about 12 550 mm at MAW and 11 963 mm at CHE for the period 1989-2010, as deduced from the available measurements at MAW. The changes in the Indian Ocean temperature have a profound effect on the rainfall in the region, and the contribution from the Arabian Sea temperature and moisture is remarkable in this respect, as analysed with a multivariate regression procedure for the period 1973-2019. The changes in land cover are another important aspect of this shift in rainfall pattern, as we find a noticeable reduction in vegetation area in northeast India in the past two decades, implying the human influence on recent climate change.

We compare global‐scale changes in satellite estimates of the temperature of the lower troposphere (TLT) with model simulations of forced and unforced TLT changes. While previous work has focused on a single period of record, we select analysis timescales ranging from 10 to 32 years, and then compare all possible observed TLT trends on each timescale with corresponding multi‐model distributions of forced and unforced trends. We use observed estimates of the signal component of TLT changes and model estimates of climate noise to calculate timescale‐dependent signal‐to‐noise ratios (S/N). These ratios are small (less than 1) on the 10‐year timescale, increasing to more than 3.9 for 32‐year trends. This large change in S/N is primarily due to a decrease in the amplitude of internally generated variability with increasing trend length. Because of the pronounced effect of interannual noise on decadal trends, a multi‐model ensemble of anthropogenically‐forced simulations displays many 10‐year periods with little warming. A single decade of observational TLT data is therefore inadequate for identifying a slowly evolving anthropogenic warming signal. Our results show that temperature records of at least 17 years in length are required for identifying human effects on global‐mean tropospheric temperature. Key Points Models run with human forcing can produce 10‐year periods with little warming S/N ratios for tropospheric temp. are ∼1 for 10‐yr trends, ∼4 for 32‐yr trends Trends >17 yrs are required for identifying human effects on tropospheric temp

Quantification of the uncertainties in future climate projections is crucial for the implementation of climate policies. Here a review of projections of global temperature change over the twenty-first century is provided for the six illustrative emission scenarios from the Special Report on Emissions Scenarios (SRES) that assume no policy intervention, based on the latest generation of coupled general circulation models, climate models of intermediate complexity, and simple models, and uncertainty ranges and probabilistic projections from various published methods and models are assessed. Despite substantial improvements in climate models, projections for given scenarios on average have not changed much in recent years. Recent progress has, however, increased the confidence in uncertainty estimates and now allows a better separation of the uncertainties introduced by scenarios, physical feedbacks, carbon cycle, and structural uncertainty. Projection uncertainties are now constrained by observations and therefore consistent with past observed trends and patterns. Future trends in global temperature resulting from anthropogenic forcing over the next few decades are found to be comparably well constrained. Uncertainties for projections on the century time scale, when accounting for structural and feedback uncertainties, are larger than captured in single models or methods. This is due to differences in the models, the sources of uncertainty taken into account, the type of observational constraints used, and the statistical assumptions made. It is shown that as an approximation, the relative uncertainty range for projected warming in 2100 is the same for all scenarios. Inclusion of uncertainties in carbon cycle–climate feedbacks extends the upper bound of the uncertainty range by more than the lower bound.

Data from the satellite-based Special Sensor Microwave Imager (SSM/I) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/m² per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated \"fingerprint\" pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/I data. Experiments in which forcing factors are varied individually suggest that this fingerprint \"match\" is primarily due to human-caused increases in greenhouse gases and not to solar forcing or recovery from the eruption of Mount Pinatubo. Our findings provide preliminary evidence of an emerging anthropogenic signal in the moisture content of earth's atmosphere.

In a recent multimodel detection and attribution (D''A) study using the pooled results from 22 different climate models, the simulated \"fingerprint\" pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D''A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D''A results are sensitive to model quality. The \"top 10\" and \"bottom 10\" models are selected with three different sets of skill measures and two different ranking approaches. The entire D''A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Niño. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by \"screening\" based on model quality.

There is increasingly clear evidence that human influence has contributed substantially to the large-scale climatic changes that have occurred over the past few decades. Attention is now turning to the physical implications of the emerging anthropogenic signal. Of particular interest is the question of whether current climate models may be over- or under-estimating the amplitude of the climate system's response to external forcing, including anthropogenic. Evidence of a significant error in a model-simulated response amplitude would indicate the existence of amplifying or damping mechanisms that are inadequately represented in the model. The range of uncertainty in the factor by which we can scale model-simulated changes while remaining consistent with observed change provides an estimate of uncertainty in model-based predictions. With any model that displays a realistic level of internal variability, the problem of estimating this factor is complicated by the fact that it represents a ratio between two incompletely known quantities: both observed and simulated responses are subject to sampling uncertainty, primarily due to internal chaotic variability. Sampling uncertainty in the simulated response can be reduced, but not eliminated, through ensemble simulations. Accurate estimation of these scaling factors requires a modification of the standard \"optimal fingerprinting\" algorithm for climate change detection, drawing on the conventional \"total least squares\" approach discussed in the statistical literature. Code for both variants of optimal fingerprinting can be found on http://www.climateprediction.net/detection.[PUBLICATION ABSTRACT]

Previous analyses of sea level pressure (SLP) trends have often focused on boreal winter trends. Here we demonstrate that externally‐forced SLP trends are observed in all four seasons, with simulated and observed decreases in SLP at high latitudes and increases elsewhere. We find that the observed pattern of seasonal mean zonal mean SLP changes is inconsistent with simulated internal variability, and we detect anthropogenic influence independently of natural influence on SLP. When we divide the globe into the mid‐ and high‐latitude regions of both hemispheres and the tropics and subtropics, we find that external influence is only detectable in the low‐latitude region, where models and observations show increasing trends in SLP, and where internal variability is low, and not in the mid‐ and high‐latitude regions of either hemisphere. Low‐latitude increases in SLP, which are significant compared to internal variability, but which have previously received little attention, could have important regional climate impacts.

Past research has shown that the dominant influence on recent global climate changes is from anthropogenic greenhouse gas increases with implications for future increases in global temperatures. One mitigation proposal is to reduce black carbon aerosol emissions. How much warming can be offset by controlling black carbon is unclear, especially as its influence on past climate has not been previously unambiguously detected. In this study observations of near-surface warming over the last century are compared with simulations using a climate model, HadGEM1. In the simulations black carbon, from fossil fuel and bio-fuel sources (fBC), produces a positive radiative forcing of about +0.25 Wm−2 over the 20th century, compared with +2.52 Wm−2 for well mixed greenhouse gases. A simulated warming of global mean near-surface temperatures over the twentieth century from fBC of 0.14 ± 0.1 K compares with 1.06 ± 0.07 K from greenhouse gases, −0.58 ± 0.10 K from anthropogenic aerosols, ozone and land use changes and 0.09 ± 0.09 K from natural influences. Using a detection and attribution methodology, the observed warming since 1900 has detectable influences from anthropogenic and natural factors. Fossil fuel and bio-fuel black carbon is found to have a detectable contribution to the warming over the last 50 yr of the 20th century, although the results are sensitive to the period being examined as fBC is not detected for the later fifty year period ending in 2006. The attributed warming of fBC was found to be consistent with the warming from fBC unscaled by the detection analysis. This study suggests that there is a possible significant influence from fBC on global temperatures, but its influence is small compared to that from greenhouse gas emissions.

A probability distribution for values of the effective climate sensitivity, with a lower bound of 1.6 K (5th percentile), is obtained on the basis of the increase in ocean heat content in recent decades from analyses of observed interior-ocean temperature changes, surface temperature changes measured since 1860, and estimates of anthropogenic and natural radiative forcing of the climate system. Radiative forcing is the greatest source of uncertainty in the calculation; the result also depends somewhat on the rate of ocean heat uptake in the late nineteenth century, for which an assumption is needed as there is no observational estimate. Because the method does not use the climate sensitivity simulated by a general circulation model, it provides an independent observationally based constraint on this important parameter of the climate system.