External control of 20th century temperature by natural and anthropogenic forcings

The competition between anthropogenic aerosol and greenhouse gas climate forcing is revealed by North Pacific water-mass changes

Modeled water-mass changes in the North Pacific thermocline, both in the subsurface and at the surface, reveal the impact of the competition between anthropogenic aerosols (AAs) and greenhouse gases (GHGs) over the past 6 decades. The AA effect overwhelms the GHG effect during 1950-1985 in driving salinity changes on density surfaces, while after 1985 the GHG effect dominates. These subsurface water-mass changes are traced back to changes at the surface, of which ~70% stems from the migration of density surface outcrops, equatorward due to regional cooling by AAs and subsequent poleward due to warming by GHGs. Ocean subduction connects these surface outcrop changes to the main thermocline. Both observations and models reveal this transition in climate forcing around 1985 and highlight the important role of AA climate forcing on our oceans' water masses.

Deep generative model super-resolves spatially correlated multiregional climate data

Super-resolving the coarse outputs of global climate simulations, termed downscaling, is crucial in making political and social decisions on systems requiring long-term climate change projections. Existing fast super-resolution techniques, however, have yet to preserve the spatially correlated nature of climatological data, which is particularly important when we address systems with spatial expanse, such as the development of transportation infrastructure. Herein, we show an adversarial network-based machine learning enables us to correctly reconstruct the inter-regional spatial correlations in downscaling with high magnification of up to 50 while maintaining pixel-wise statistical consistency. Direct comparison with the measured meteorological data of temperature and precipitation distributions reveals that integrating climatologically important physical information improves the downscaling performance, which prompts us to call this approach [Formula: see text]SRGAN (Physics Informed Super-Resolution Generative Adversarial Network). The proposed method has a potential application to the inter-regionally consistent assessment of the climate change impact. Additionally, we present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field, referred to as [Formula: see text]SRGAN (Precipitation Source Inaccessible SRGAN). Remarkably, this method demonstrates unexpectedly good downscaling performance for the precipitation field.

Estimating Ocean Heat Uptake Using Boundary Green's Functions: A Perfect-Model Test of the Method

Ocean heat uptake is caused by "excess heat" being added to the ocean surface by air-sea fluxes and then carried to depths by ocean transports. One way to estimate excess heat in the ocean is to propagate observed sea surface temperature (SST) anomalies downward using a Green's function (GF) representation of ocean transports. Taking a "perfect-model" approach, we test this GF method using a historical simulation, in which the true excess heat is diagnosed. We derive GFs from two approaches: (a) simulating GFs using idealized tracers, and (b) inferring GFs from simulated CFCs and climatological tracers. In the model world, we find that combining simulated GFs with SST anomalies reconstructs the Indo-Pacific excess heat with a root-mean-square error of 26% for depth-integrated changes; the corresponding number is 34% for inferred GFs. Simulated GFs are inaccurate because they are coarse grained in space and time to reduce computational cost. Inferred GFs are inaccurate because observations are insufficient constraints. Both kinds of GFs neglect the slowdown of the North Atlantic heat uptake as the ocean warms up. SST boundary conditions contain redistributive cooling in the Southern Ocean, which causes an underestimate of heat uptake there. All these errors are of comparable magnitude, and tend to compensate each other partially. Inferred excess heat is not sensitive to: (a) small changes in the shape of prior GFs, or (b) additional constraints from SF₆ and bomb ¹⁴C.

Functional traits underlying performance variations in the overwintering of the cosmopolitan invasive plant water hyacinth ( Eichhornia crassipes ) under climate warming and water drawdown

Reports of the Intergovernmental Panel on Climate Change (IPCC) indicate that temperature rise is still the general trend of the global climate in the 21st century. Invasive species may benefit from the increase in temperature, as climate can be viewed as a resource, and the increase in the available resources favors the invasibility of invasive species. This study aimed to assess the overwintering growth of the cosmopolitan invasive plant water hyacinth (Eichhornia crassipes) at its northern boundary. Using E. crassipes as a model plant, a cross‐year mesocosm experiment was conducted to determine 17 plant functional traits, including growth, morphological, root topological, photosynthetic, and stoichiometric traits, under climate warming (ambient, temperature rises of 1.5°C and 3.0°C), and water drawdown or water withdrawal (water depths of 1, 10, and 20 cm) treatments. The overwintering growth of E. crassipes was facilitated by climate warming and proper water drawdown, and climate warming played a leading role. A temperature rises of 3.0°C and a water depth of 10 cm were the most suitable conditions for the overwintering and rooting behavior of the plant. Controlling the temperature to within 1.5°C, an ambitious goal for China, still facilitated the overwintering of E. crassipes. With climate warming, the plant can overwinter successfully, which possibly assists it in producing and spreading new ramets in the vernal flood season. The new rooting behavior induced by ambient low temperature may be viewed as a unique growth adaptation strategy for a niche change, as it helps these plants invade empty niches left by dead free‐floating plants on the water surface following winter freezes. With continued global warming, the distribution of the plant may expand northward, and eradication of the plant during the winter water drawdown period may be a more effective strategy.

Climatic niche shifts in introduced species

Predictions of future biological invasions often rely on the assumption that introduced species establish only under climatic conditions similar to those in their native range. To date, 135 studies have tested this assumption of 'niche conservatism', yielding contradictory results. Here we revisit this literature, consider the evidence for niche shifts, critically assess the methods used, and discuss the authors' interpretations of niche shifts. We find that the true frequency of niche shifts remains unknown because of diverging interpretations of similar metrics, conceptual issues biasing conclusions towards niche conservatism, and the use of climatic data that may not be biologically meaningful. We argue that these issues could be largely addressed by focussing on trends or relative degrees of niche change instead of dichotomous classifications (shift versus no shift), consistently and transparently including non-analogous climates, and conducting experimental studies on mismatches between macroclimates and microclimates experienced by the study organism. Furthermore, an observed niche shift may result either from species filling a greater part of their fundamental niche during the invasion (a 'realised niche shift') or from rapid evolution of traits adapting species to novel climates in the introduced range (a 'fundamental niche shift'). Currently, there is no conclusive evidence distinguishing between these potential mechanisms of niche shifts. We outline how these questions may be addressed by combining computational analyses and experimental evidence.

Recent Advances in TiO2-Based Heterojunctions for Photocatalytic CO2 Reduction With Water Oxidation: A Review

Photocatalytic conversion of CO₂ into solar fuels has gained increasing attention due to its great potential for alleviating the energy and environmental crisis at the same time. The low-cost TiO₂ with suitable band structure and high resistibility to light corrosion has proven to be very promising for photoreduction of CO₂ using water as the source of electrons and protons. However, the narrow spectral response range (ultraviolet region only) as well as the rapid recombination of photo-induced electron-hole pairs within pristine TiO₂ results in the low utilization of solar energy and limited photocatalytic efficiency. Besides, its low selectivity toward photoreduction products of CO₂ should also be improved. Combination of TiO₂ with other photoelectric active materials, such as metal oxide/sulfide semiconductors, metal nanoparticles and carbon-based nanostructures, for the construction of well-defined heterostructures can enhance the quantum efficiency significantly by promoting visible light adsorption, facilitating charge transfer and suppressing the recombination of charge carriers, resulting in the enhanced photocatalytic performance of the composite photocatalytic system. In addition, the adsorption and activation of CO₂ on these heterojunctions are also promoted, therefore enhancing the turnover frequency (TOF) of CO₂ molecules, so as to the improved selectivity of photoreduction products. This review focus on the recent advances of photocatalytic CO₂ reduction via TiO₂-based heterojunctions with water oxidation. The rational design, fabrication, photocatalytic performance and CO₂ photoreduction mechanisms of typical TiO₂-based heterojunctions, including semiconductor-semiconductor (S-S), semiconductor-metal (S-M), semiconductor-carbon group (S-C) and multicomponent heterojunction are reviewed and discussed. Moreover, the TiO₂-based phase heterojunction and facet heterojunction are also summarized and analyzed. In the end, the current challenges and future prospects of the TiO₂-based heterostructures for photoreduction of CO₂ with high efficiency, even for practical application are discussed.

COVID-19 lockdowns induced land surface temperature variability in mega urban agglomerations in India

The COVID-19 pandemic forced a nationwide lockdown in India for months when close to 1.3 billion people were confined to their homes. An abrupt halt in the majority of the urban activities reduced the generation of anthropogenic heat which often exacerbates the Urban Heat Island (UHI) effect in the urban pockets of the country. We studied the lockdown impact on seven highly populated and polluted mega urban agglomerations across India, namely Delhi, Ahmedabad, Hyderabad, Kolkata, Mumbai, Bengaluru and Chennai, using near-anniversary Landsat 8 data. The results revealed that the lockdowns have improved the air quality and reduced the Land Surface Temperature (LST) and hence the UHI effect over these cities. Each of the cities experienced an improved Air Quality Index (AQI) ranging from 18 to 151 units except Chennai (with a marginal 8 units increase in AQI), a decrease in mean LST in the range of 0.27 °C to 7.06 °C except Kolkata which showed an increment by ∼4 °C, and a reduction in daily averaged air temperature ranging from 0.3 °C to 10.88 °C except Hyderabad which witnessed an increase of 0.09 °C during the lockdown (April 2020) compared to the previous years (April 2019 and 2018). Delhi exhibited the maximum positive impact of the lockdown in all aspects with two-fold improved air quality, and Ahmedabad showed the least improvement. In addition to the variations in regional land use and land cover and proportion of essential industries that remained operational throughout the lockdown, the geographic location, topography, local meteorology and climate were some of the other factors also responsible for either aiding or overcompensating the large scale LST variabilities observed in these cities. These results hint at an unprecedented opportunity to evaluate the effectiveness of periodic planned lockdowns as a possible mitigating measure to reduce LST spikes and degraded air quality in urban areas in the future.

Evaluating the Impact of Climate Change on Water Productivity of Maize in the Semi-Arid Environment of Punjab, Pakistan

Impact assessments on climate change are essential for the evaluation and management of irrigation water in farming practices in semi-arid environments. This study was conducted to evaluate climate change impacts on water productivity of maize in farming practices in the Lower Chenab Canal (LCC) system. Two fields of maize were selected and monitored to calibrate and validate the model. A water productivity analysis was performed using the Soil–Water–Atmosphere–Plant (SWAP) model. Baseline climate data (1980–2010) for the study site were acquired from the weather observatory of the Pakistan Meteorological Department (PMD). Future climate change data were acquired from the Hadley Climate model version 3 (HadCM3). Statistical downscaling was performed using the Statistical Downscaling Model (SDSM) for the A2 and B2 scenarios of HadCM3. The water productivity assessment was performed for the midcentury (2040–2069) scenario. The maximum increase in the average maximum temperature (Tmax) and minimum temperature (Tmin) was found in the month of July under the A2 and B2 scenarios. The scenarios show a projected increase of 2.8 °C for Tmax and 3.2 °C for Tmin under A2 as well as 2.7 °C for Tmax and 3.2 °C for Tmin under B2 for the midcentury. Similarly, climate change scenarios showed that temperature is projected to decrease, with the average minimum and maximum temperatures of 7.4 and 6.4 °C under the A2 scenario and 7.7 and 6.8 °C under the B2 scenario in the middle of the century, respectively. However, the highest precipitation will decrease by 56 mm under the A2 and B2 scenarios in the middle of the century for the month of September. The input and output data of the SWAP model were processed in R programming for the easy working of the model. The negative impact of climate change was found under the A2 and B2 scenarios during the midcentury. The maximum decreases in Potential Water Productivity (WPET) and Actual Water Productivity (WPAI) from the baseline period to the midcentury scenario of 1.1 to 0.85 kgm−3 and 0.7 to 0.56 kgm−3 were found under the B2 scenario. Evaluation of irrigation practices directs the water managers in making suitable water management decisions for the improvement of water productivity in the changing climate.

Rapid urbanization and associated impacts on land surface temperature changes over Bhubaneswar Urban District, India

Urban settlements are the socio-economic drivers of human community. However, rapid urban expansion during recent times has a profound effect on regional as well as global rise in temperatures. Remote sensing is a powerful tool to monitor and quantify the expansion of urban built-up area and assess its effects on regional heating. Landsat-derived Land Use Land Cover (LULC) maps have shown a very rapid urbanization in Bhubaneswar, a tier-2 city and the capital of Odisha in the eastern part of India, almost doubling over the last 15 years. Our study on the effects of urbanization reveals that during the period of 2003-2017, the built-up area in the urban district of Bhubaneswar has increased by about 77% at the cost of natural vegetative cover and agricultural/fallow land. This affects the behavior of several climate variables over the urban centers with a clear distinction in these variables observed between the urban and surrounding rural settlements. Land surface temperature (LST) derived from the thermal bands of Landsat satellites at a spatial resolution of 30 m is used for the analysis over the area of interest. The Bhubaneswar airport (urban reference) has shown an increase in mean summer LST by 8% over the study period of 15 years, and 9.84% in LST in the last 5 years from 2013 to 2017 alone. From the present study, it is inferred that it is essential to adopt a sustainable approach for any urban growth to restrain its effect on urban temperature rise.

The Atmosphere

The atmosphere is composed of nitrogen, oxygen and argon, a variety of trace gases, and particles or aerosols from a variety of sources. Reactive, trace gases have short mean residence time in the atmosphere and large spatial and temporal variations in concentration. Many trace gases are removed by reaction with hydroxyl radical and deposition in rainfall or dryfall at the Earth's surface. The upper atmosphere, the stratosphere, contains ozone that screens ultraviolet light from the Earth's surface. Chlorofluorocarbons released by humans lead to the loss of stratospheric ozone, which might eventually render the Earth's land surface uninhabitable. Changes in the composition of the atmosphere, especially rising concentrations of CO₂, CH₄, and N₂O, will lead to climatic changes over much of the Earth's surface.

Quantifying stochastic uncertainty in detection time of human-caused climate signals

Large initial condition ensembles of a climate model simulation provide many different realizations of internal variability noise superimposed on an externally forced signal. They have been used to estimate signal emergence time at individual grid points, but are rarely employed to identify global fingerprints of human influence. Here we analyze 50- and 40-member ensembles performed with 2 climate models; each was run with combined human and natural forcings. We apply a pattern-based method to determine signal detection time [Formula: see text] in individual ensemble members. Distributions of [Formula: see text] are characterized by the median [Formula: see text] and range [Formula: see text], computed for tropospheric and stratospheric temperatures over 1979 to 2018. Lower stratospheric cooling-primarily caused by ozone depletion-yields [Formula: see text] values between 1994 and 1996, depending on model ensemble, domain (global or hemispheric), and type of noise data. For greenhouse-gas-driven tropospheric warming, larger noise and slower recovery from the 1991 Pinatubo eruption lead to later signal detection (between 1997 and 2003). The stochastic uncertainty [Formula: see text] is greater for tropospheric warming (8 to 15 y) than for stratospheric cooling (1 to 3 y). In the ensemble generated by a high climate sensitivity model with low anthropogenic aerosol forcing, simulated tropospheric warming is larger than observed; detection times for tropospheric warming signals in satellite data are within [Formula: see text] ranges in 60% of all cases. The corresponding number is 88% for the second ensemble, which was produced by a model with even higher climate sensitivity but with large aerosol-induced cooling. Whether the latter result is physically plausible will require concerted efforts to reduce significant uncertainties in aerosol forcing.

Correcting datasets leads to more homogeneous early-twentieth-century sea surface warming

Existing estimates of sea surface temperatures (SSTs) indicate that, during the early twentieth century, the North Atlantic and northeast Pacific oceans warmed by twice the global average, whereas the northwest Pacific Ocean cooled by an amount equal to the global average^1-4. Such a heterogeneous pattern suggests first-order contributions from regional variations in forcing or in ocean-atmosphere heat fluxes^5,6. These older SST estimates are, however, derived from measurements of water temperatures in ship-board buckets, and must be corrected for substantial biases^7-9. Here we show that correcting for offsets among groups of bucket measurements leads to SST variations that correlate better with nearby land temperatures and are more homogeneous in their pattern of warming. Offsets are identified by systematically comparing nearby SST observations among different groups¹⁰. Correcting for offsets in German measurements decreases warming rates in the North Atlantic, whereas correcting for Japanese measurement offsets leads to increased and more uniform warming in the North Pacific. Japanese measurement offsets in the 1930s primarily result from records having been truncated to whole degrees Celsius when the records were digitized in the 1960s. These findings underscore the fact that historical SST records reflect both physical and social dimensions in data collection, and suggest that further opportunities exist for improving the accuracy of historical SST records^9,11.

Consistent multi-decadal variability in global temperature reconstructions and simulations over the Common Era

Multi-decadal surface temperature changes may be forced by natural as well as anthropogenic factors, or arise unforced from the climate system. Distinguishing these factors is essential for estimating sensitivity to multiple climatic forcings and the amplitude of the unforced variability. Here we present 2,000-year-long global mean temperature reconstructions using seven different statistical methods that draw from a global collection of temperature-sensitive paleoclimate records. Our reconstructions display synchronous multi-decadal temperature fluctuations, which are coherent with one another and with fully forced CMIP5 millennial model simulations across the Common Era. The most significant attribution of pre-industrial (1300-1800 CE) variability at multi-decadal timescales is to volcanic aerosol forcing. Reconstructions and simulations qualitatively agree on the amplitude of the unforced global mean multi-decadal temperature variability, thereby increasing confidence in future projections of climate change on these timescales. The largest warming trends at timescales of 20 years and longer occur during the second half of the 20^th century, highlighting the unusual character of the warming in recent decades.

A greening world enhances the surface-air temperature difference

The surface-air temperature difference (Ts-Ta) is a critical variable for tracking climatic and environmental change. Vegetation has unavoidably affected the temperature by altering surface properties, while the magnitude of this effect has remained unknown. This study aimed to investigate the patterns of global Ts-Ta and quantify the contribution of vegetation change. Trend analysis, correlation analysis and a trajectory-based method were adopted for the investigation. The results demonstrated that the global Ts-Ta decreased by -0.140 K from 2001 to 2016. The greening trend covered 24.46% of the land and played a profound role in changing Ts-Ta. In particular, vegetation changes resulted in -0.0022 K, -0.0092 K and - 0.0043 K of the Ts-Ta decreases at the global, greening and browning levels, respectively accounting for 11.58%, 35.38% and 20.38% of the total decrease. Physically, vegetation influenced Ts-Ta mainly by altering atmospheric properties, rather than surface properties. Specifically, the greening of the surface reduced the albedo at a rate of -0.0003/year over 20% of the global land and enhanced atmospheric water vapor by 3 × 10^-5 g/m³ over approximately 40% of the land. Meanwhile, the effect of vegetation change varied with coverage. A reduction in albedo caused by vegetation change occurred equally over different vegetated conditions, while the enhancement of atmospheric water vapor occurred mainly in sparsely (0.10 < NDVI < 0.30) and densely (0.55 < NDVI < 0.70) vegetated regions. Under these conditions, the vegetation change mainly affected Ts-Ta in sparsely vegetated regions (NDVI < 0.4). The results of this study are helpful for understanding the physical mechanism behind changes in global Ts-Ta and support climatic adaptation and environmental management.

The influence of climate change on waterborne disease and Legionella: a review

Climate change is predicted to have a major impact on people's lives with the recent extreme weather events and varying abnormal temperature profiles across the world raising concerns. The impacts of global warming are already being observed, from rising sea levels and melting snow and ice to changing weather patterns. Scientists state unequivocally that these trends cannot be explained by natural variability in climate alone. Human activities, especially the burning of fossil fuels, have warmed the earth by dramatically increasing concentrations of heat-trapping gases in the atmosphere; as these concentrations increase, the more the earth will warm. Climate change and related extreme weather events are being exacerbated sooner than has previously been considered and are already adversely affecting ecosystems and human health by increasing the burden and type of disease at a local level. Changes to the marine environment and freshwater supplies already affect significant parts of the world's population and warmer temperatures, especially in more temperate regions, may see an increased spread and transmission of diseases usually associated with warmer climes including, for example, cholera and malaria; these impacts are likely to become more severe in a greater number of countries. This review discusses the impacts of climate change including changes in infectious disease transmission, patterns of waterborne diseases and the likely consequences of climate change due to warmer water, drought, higher rainfall, rising sea levels and flooding.

Causes of irregularities in trends of global mean surface temperature since the late 19th century

The time series of monthly global mean surface temperature (GST) since 1891 is successfully reconstructed from known natural and anthropogenic forcing factors, including internal climate variability, using a multiple regression technique. Comparisons are made with the performance of 40 CMIP5 models in predicting GST. The relative contributions of the various forcing factors to GST changes vary in time, but most of the warming since 1891 is found to be attributable to the net influence of increasing greenhouse gases and anthropogenic aerosols. Separate statistically independent analyses are also carried out for three periods of GST slowdown (1896-1910, 1941-1975, and 1998-2013 and subperiods); two periods of strong warming (1911-1940 and 1976-1997) are also analyzed. A reduction in total incident solar radiation forcing played a significant cooling role over 2001-2010. The only serious disagreements between the reconstructions and observations occur during the Second World War, especially in the period 1944-1945, when observed near-worldwide sea surface temperatures (SSTs) may be significantly warm-biased. In contrast, reconstructions of near-worldwide SSTs were rather warmer than those observed between about 1907 and 1910. However, the generally high reconstruction accuracy shows that known external and internal forcing factors explain all the main variations in GST between 1891 and 2015, allowing for our current understanding of their uncertainties. Accordingly, no important additional factors are needed to explain the two main warming and three main slowdown periods during this epoch.

Natural Variation and Genetics of Photoperiodism in Wyeomyia smithii

Seasonal change in the temperate and polar regions of Earth determines how the world looks around us and, in fact, how we live our day-to-day lives. For biological organisms, seasonal change typically involves complex physiological and metabolic reorganization, the majority of which is regulated by photoperiodism. Photoperiodism is the ability of animals and plants to use day length or night length, resulting in life-historical transformations, including seasonal development, migration, reproduction, and dormancy. Seasonal timing determines not only survival and reproductive success but also the structure and organization of complex communities and, ultimately, the biomes of Earth. Herein, a small mosquito, Wyeomyia smithii, that lives only in the water-filled leaves of a carnivorous plant over a wide geographic range, is used to explore the genetic and evolutionary basis of photoperiodism. Photoperiodism in W. smithii is considered in the context of its historical biogeography in nature to examine the startling finding that recent rapid climate change can drive genetic change in plants and animals at break-neck speed, and to challenge the ponderous 80+ year search for connections between daily and seasonal time-keeping mechanisms. Finally, a model is proposed that reconciles the seemingly disparate 24-h daily clock driven by the invariant rotation of Earth about its axis with the evolutionarily flexible seasonal timer orchestrated by variable seasonality driven by the rotation of Earth about the Sun.

A coupled human-Earth model perspective on long-term trends in the global marine fishery

The global wild marine fish harvest increased fourfold between 1950 and a peak value near the end of the 20th century, reflecting interactions between anthropogenic and ecological forces. Here, we examine these interactions in a bio-energetically constrained, spatially and temporally resolved model of global fisheries. We conduct historical hindcasts with the model, which suggest that technological progress can explain most of the 20th century increase of fish harvest. In contrast, projections extending this rate of technological progress into the future under open access suggest a long-term decrease in harvest due to over-fishing. Climate change is predicted to gradually decrease the global fish production capacity, though our model suggests that this is of secondary importance to social and economic factors. Our study represents a novel way to integrate human-ecological interactions within a single model framework for long-term simulations.

Contributions of natural and anthropogenic radiative forcing to mass loss of Northern Hemisphere mountain glaciers and quantifying their uncertainties

Observational evidence indicates that a number of glaciers have lost mass in the past. Given that glaciers are highly impacted by the surrounding climate, human-influenced global warming may be partly responsible for mass loss. However, previous research studies have been limited to analyzing the past several decades, and it remains unclear whether past glacier mass losses are within the range of natural internal climate variability. Here, we apply an optimal fingerprinting technique to observed and reconstructed mass losses as well as multi-model general circulation model (GCM) simulations of mountain glacier mass to detect and attribute past glacier mass changes. An 8,800-year control simulation of glaciers enabled us to evaluate detectability. The results indicate that human-induced increases in greenhouse gases have contributed to the decreased area-weighted average masses of 85 analyzed glaciers. The effect was larger than the mass increase caused by natural forcing, although the contributions of natural and anthropogenic forcing to decreases in mass varied at the local scale. We also showed that the detection of anthropogenic or natural influences could not be fully attributed when natural internal climate variability was taken into account.

Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.

Variations in global temperature and precipitation for the period of 1948 to 2010

Climate change has impacts on both natural and human systems. Accurate information regarding variations in precipitation and temperature is essential for identifying and understanding these potential impacts. This research applied Mann-Kendall, rescaled range analysis and wave transform methods to analyze the trends and periodic properties of global and regional surface air temperature (SAT) and precipitation (PR) over the period of 1948 to 2010. The results show that 65.34% of the area studied exhibits significant warming trends (p < 0.05) while only 3.18% of the area exhibits significant cooling trends. The greatest warming trends are observed in Antarctica (0.32 °C per decade) and Middle Africa (0.21 °C per decade). Notably, 62.26% of the area became wetter, while 22.01% of the area shows drying trends. Northern Europe shows the largest precipitation increase, 12.49 mm per decade. Western Africa shows the fastest drying, -21.05 mm per decade. The rescaled range analysis reveals large areas that show persistent warming trends; this behavior in SAT is more obvious than that in PR. Wave transform results show that a 1-year period of SAT variation dominates in all regions, while inconsistent 0.5-year bands are observed in East Asia, Middle Africa, and Southeast Asia. In PR, significant power in the wavelet power spectrum at a 1-year period was observed in 17 regions, i.e., in all regions studied except Western Europe, where precipitation is instead characterized by 0.5-year and 0.25-year periods. Overall, the variations in SAT and PR can be consistent with the combined impacts of natural and anthropogenic factors, such as atmospheric concentrations of greenhouse gases, the internal variability of climate system, and volcanic eruptions.

Spatio-temporal analyses of Symbiodinium physiology of the coral Pocillopora verrucosa along large-scale nutrient and temperature gradients in the Red Sea

Algal symbionts (zooxanthellae, genus Symbiodinium) of scleractinian corals respond strongly to temperature, nutrient and light changes. These factors vary greatly along the north-south gradient in the Red Sea and include conditions, which are outside of those typically considered optimal for coral growth. Nevertheless, coral communities thrive throughout the Red Sea, suggesting that zooxanthellae have successfully acclimatized or adapted to the harsh conditions they experience particularly in the south (high temperatures and high nutrient supply). As such, the Red Sea is a region, which may help to better understand how zooxanthellae and their coral hosts successfully acclimatize or adapt to environmental change (e.g. increased temperatures and localized eutrophication). To gain further insight into the physiology of coral symbionts in the Red Sea, we examined the abundance of dominant Symbiodinium types associated with the coral Pocillopora verrucosa, and measured Symbiodinium physiological characteristics (i.e. photosynthetic processes, cell density, pigmentation, and protein composition) along the latitudinal gradient of the Red Sea in summer and winter. Despite the strong environmental gradients from north to south, our results demonstrate that Symbiodinium microadriaticum (type A1) was the predominant species in P. verrucosa along the latitudinal gradient. Furthermore, measured physiological characteristics were found to vary more with prevailing seasonal environmental conditions than with region-specific differences, although the measured environmental parameters displayed much higher spatial than temporal variability. We conclude that our findings might present the result of long-term acclimatization or adaptation of S. microadriaticum to regionally specific conditions within the Red Sea. Of additional note, high nutrients in the South correlated with high zooxanthellae density indicating a compensation for a temperature-driven loss of photosynthetic performance, which may prove promising for the resilience of these corals under increase of temperature increase and eutrophication.

Extended season for northern butterflies

Butterflies are like all insects in that they are temperature sensitive and a changing climate with higher temperatures might effect their phenology. Several studies have found support for earlier flight dates among the investigated species. A comparative study with data from a citizen science project, including 66 species of butterflies in Sweden, was undertaken, and the result confirms that most butterfly species now fly earlier during the season. This is especially evident for butterflies overwintering as adults or as pupae. However, the advancement in phenology is correlated with flight date, and some late season species show no advancement or have even postponed their flight dates and are now flying later in the season. The results also showed that latitude had a strong effect on the adult flight date, and most of the investigated species showed significantly later flights towards the north. Only some late flying species showed an opposite trend, flying earlier in the north. A majority of the investigated species in this study showed a general response to temperature and advanced their flight dates with warmer temperatures (on average they advanced their flight dates by 3.8 days/°C), although not all species showed this response. In essence, a climate with earlier springs and longer growing seasons seems not to change the appearance patterns in a one-way direction. We now see butterflies on the wings both earlier and later in the season and some consequences of these patterns are discussed. So far, studies have concentrated mostly on early season butterfly-plant interactions but also late season studies are needed for a better understanding of long-term population consequences.

A time-series analysis of the 20th century climate simulations produced for the IPCC's Fourth Assessment Report

In this paper evidence of anthropogenic influence over the warming of the 20th century is presented and the debate regarding the time-series properties of global temperatures is addressed in depth. The 20th century global temperature simulations produced for the Intergovernmental Panel on Climate Change’s Fourth Assessment Report and a set of the radiative forcing series used to drive them are analyzed using modern econometric techniques. Results show that both temperatures and radiative forcing series share similar time-series properties and a common nonlinear secular movement. This long-term co-movement is characterized by the existence of time-ordered breaks in the slope of their trend functions. The evidence presented in this paper suggests that while natural forcing factors may help explain the warming of the first part of the century, anthropogenic forcing has been its main driver since the 1970’s. In terms of Article 2 of the United Nations Framework Convention on Climate Change, significant anthropogenic interference with the climate system has already occurred and the current climate models are capable of accurately simulating the response of the climate system, even if it consists in a rapid or abrupt change, to changes in external forcing factors. This paper presents a new methodological approach for conducting time-series based attribution studies.

Climate, copepods and seabirds in the boreal Northeast Atlantic - current state and future outlook

The boreal Northeast Atlantic is strongly affected by current climate change, and large shifts in abundance and distribution of many organisms have been observed, including the dominant copepod Calanus finmarchicus, which supports the grazing food web and thus many fish populations. At the same time, large-scale declines have been observed in many piscivorous seabirds, which depend on abundant small pelagic fish. Here, we combine predictions from a niche model of C. finmarchicus with long-term data on seabird breeding success to link trophic levels. The niche model shows that environmental suitability for C. finmarchicus has declined in southern areas with large breeding seabird populations (e.g. the North Sea), and predicts that this decline is likely to spread northwards during the 21st century to affect populations in Iceland and the Faroes. In a North Sea colony, breeding success of three common piscivorous seabird species [black-legged kittiwake (Rissa tridactyla), common guillemot (Uria aalge) and Atlantic puffin (Fratercula arctica)] was strongly positively correlated with local environmental suitability for C. finmarchicus, whereas this was not the case at a more northerly colony in west Norway. Large seabird populations seem only to occur where C. finmarchicus is abundant, and northward distributional shifts of common boreal seabirds are therefore expected over the coming decades. Whether or not population size can be maintained depends on the dispersal ability and inclination of these colonial breeders, and on the carrying capacity of more northerly areas in a warmer climate.

Identifying human influences on atmospheric temperature

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

The Politics of the Anthropogenic

The term anthropogenic takes its meaning from an implied contrast to an idealist notion of nature as separate from humans and endowed with a timeless or cyclical equilibrium. In recent decades, however, scientists have concluded that human influences now dominate nature at global and geological scales, reflected in the contention that Earth has entered a new epoch called the Anthropocene. Anthropogenic global warming is central to these developments, and the United Nations Framework Convention on Climate Change obligates the international community of nations to “prevent dangerous anthropogenic interference with the climate system.” Scientific debates surrounding anthropogenic impacts on the environment have a much longer history, however, revealing chronic empirical difficulties with the human-nature dualism combined with an inability to overcome it conceptually. Anthropologists have a key role to play in emerging transdisciplinary efforts to understand the anthropogenic across scales from the molecular to the global.

On the applicability of temperature and precipitation data from CMIP3 for China

Global Circulation Models (GCMs) contributed to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and are widely used in global change research. This paper assesses the performance of the AR4 GCMs in simulating precipitation and temperature in China from 1960 to 1999 by comparison with observed data, using system bias (B), root-mean-square error (RMSE), Pearson correlation coefficient (R) and Nash-Sutcliffe model efficiency (E) metrics. Probability density functions (PDFs) are also fitted to the outputs of each model. It is shown that the performance of each GCM varies to different degrees across China. Based on the skill score derived from the four metrics, it is suggested that GCM 15 (ipsl_cm4) and GCM 3 (cccma_cgcm_t63) provide the best representations of temperature and precipitation, respectively, in terms of spatial distribution and trend over 10 years. The results also indicate that users should apply carefully the results of annual precipitation and annual temperature generated by AR4 GCMs in China due to poor performance. At a finer scale, the four metrics are also used to obtain best fit scores for ten river basins covering mainland China. Further research is proposed to improve the simulation accuracy of the AR4 GCMs regarding China.

Aerosol size distributions in urban Jinan: seasonal characteristics and variations between weekdays and weekends in a heavily polluted atmosphere

Aerosol size distributions, trace gas, and PM(2.5) concentrations have been measured in urban Jinan, China, over 6 months in 2007 and 2008, covering spring, summer, fall, and winter time periods. Number concentrations of particles (10-2,500 nm) were 16,200, 13,900, 11,200, and 21,600 cm( -3) in spring, summer, fall, and winter, respectively. Compared with other urban studies, Jinan has higher number concentrations of accumulation-mode particles (100-500 nm) and particles (10-2,500 nm), but lower concentrations of ultrafine particles (10-100 nm). The number, surface and volume concentrations, and size distributions of particles showed obvious seasonal variation and are also influenced by traffic emissions. Through correlation analysis, traffic emissions are proposed to be a more important contributor to Atkien-mode and accumulation-mode particles than coal firing. Around midday, the presence of nanoparticles and new particle formation is limited to pre-existing particles from traffic emissions and the mass transport of particles from suburban and rural areas. Compared with other studies in urban areas of Europe and the USA, the variation of particle number concentration and related gas concentration in Jinan between weekdays and weekends is smaller and the reasons has been deduced.

Emergent dynamics of the climate-economy system in the Anthropocene

Global CO(2) emissions are understood to be the largest contributor to anthropogenic climate change, and have, to date, been highly correlated with economic output. However, there is likely to be a negative feedback between climate change and human wealth: economic growth is typically associated with an increase in CO(2) emissions and global warming, but the resulting climate change may lead to damages that suppress economic growth. This climate-economy feedback is assumed to be weak in standard climate change assessments. When the feedback is incorporated in a transparently simple model it reveals possible emergent behaviour in the coupled climate-economy system. Formulae are derived for the critical rates of growth of global CO(2) emissions that cause damped or long-term boom-bust oscillations in human wealth, thereby preventing a soft landing of the climate-economy system. On the basis of this model, historical rates of economic growth and decarbonization appear to put the climate-economy system in a potentially damaging oscillatory regime.

Long-term natural variability and 20th century climate change

Global mean temperature at the Earth's surface responds both to externally imposed forcings, such as those arising from anthropogenic greenhouse gases, as well as to natural modes of variability internal to the climate system. Variability associated with these latter processes, generally referred to as natural long-term climate variability, arises primarily from changes in oceanic circulation. Here we present a technique that objectively identifies the component of inter-decadal global mean surface temperature attributable to natural long-term climate variability. Removal of that hidden variability from the actual observed global mean surface temperature record delineates the externally forced climate signal, which is monotonic, accelerating warming during the 20th century.

Incorporating model quality information in climate change detection and attribution studies

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.

Decadal climate prediction (project GCEP)

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

Climate change and heat-related mortality in six cities Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change

Previous assessments of the impacts of climate change on heat-related mortality use the "delta method" to create temperature projection time series that are applied to temperature-mortality models to estimate future mortality impacts. The delta method means that climate model bias in the modelled present does not influence the temperature projection time series and impacts. However, the delta method assumes that climate change will result only in a change in the mean temperature but there is evidence that there will also be changes in the variability of temperature with climate change. The aim of this paper is to demonstrate the importance of considering changes in temperature variability with climate change in impacts assessments of future heat-related mortality. We investigate future heat-related mortality impacts in six cities (Boston, Budapest, Dallas, Lisbon, London and Sydney) by applying temperature projections from the UK Meteorological Office HadCM3 climate model to the temperature-mortality models constructed and validated in Part 1. We investigate the impacts for four cases based on various combinations of mean and variability changes in temperature with climate change. The results demonstrate that higher mortality is attributed to increases in the mean and variability of temperature with climate change rather than with the change in mean temperature alone. This has implications for interpreting existing impacts estimates that have used the delta method. We present a novel method for the creation of temperature projection time series that includes changes in the mean and variability of temperature with climate change and is not influenced by climate model bias in the modelled present. The method should be useful for future impacts assessments. Few studies consider the implications that the limitations of the climate model may have on the heat-related mortality impacts. Here, we demonstrate the importance of considering this by conducting an evaluation of the daily and extreme temperatures from HadCM3, which demonstrates that the estimates of future heat-related mortality for Dallas and Lisbon may be overestimated due to positive climate model bias. Likewise, estimates for Boston and London may be underestimated due to negative climate model bias. Finally, we briefly consider uncertainties in the impacts associated with greenhouse gas emissions and acclimatisation. The uncertainties in the mortality impacts due to different emissions scenarios of greenhouse gases in the future varied considerably by location. Allowing for acclimatisation to an extra 2 degrees C in mean temperatures reduced future heat-related mortality by approximately half that of no acclimatisation in each city.

Computational study on kinetics and mechanisms of unimolecular decomposition of succinic acid and its anhydride

The mechanisms and kinetics of unimolecular decomposition of succinic acid and its anhydride have been studied at the G2M(CC2) and microcanonical RRKM levels of theory. It was shown that the ZsgsZ conformer of succinic acid, with the Z-acid form and the gauche conformation around the central C-C bond, is its most stable conformer, whereas the lowest energy conformer with the E-acid form, ECGsZ, is only 3.1 kcal/mol higher in energy than the ZsgsZ. Three primary decomposition channels of succinic acid producing H2O + succinic anhydride with a barrier of 51.0 kcal/mol, H2O + OCC2H3COOH with a barrier of 75.7 kcal/mol and CO2 + C2H5COOH with a barrier of 71.9 kcal/mol were predicted. The dehydration process starting from the ECGCZ-conformer is found to be dominant, whereas the decarboxylation reaction starting from the ZsgsZ-conformer is only slightly less favorable. It was shown that the decomposition of succinic anhydride occurs via a concerted fragmentation mechanism (with a 69.6 kcal/mol barrier), leading to formation of CO + CO2 + C2H4 products. On the basis of the calculated potential energy surfaces of these reactions, the rate constants for unimolecular decomposition of succinic acid and its anhydride were predicted. In addition, the predicted rate constants for the unimolecular decomposition of C2H5COOH by decarboxylation (giving C2H6 + CO2) and dehydration (giving H3CCHCO + H2O) are in good agreement with available experimental data.

Current practices and future opportunities for policy on climate change and invasive species

Climate change and invasive species are often treated as important, but independent, issues. Nevertheless, they have strong connections: changes in climate and societal responses to climate change may exacerbate the impacts of invasive species, whereas invasive species may affect the magnitude, rate, and impact of climate change. We argue that the design and implementation of climate-change policy in the United States should specifically consider the implications for invasive species; conversely, invasive-species policy should address consequences for climate change. The development of such policies should be based on (1) characterization of interactions between invasive species and climate change, (2) identification of areas where climate-change policies could negatively affect invasive-species management, and (3) identification of areas where policies could benefit from synergies between climate change and invasive-species management.