LiG Metrology, Correlated Error, and the Integrity of the Global Surface Air-Temperature Record

The published 95% uncertainty of the global surface air-temperature anomaly (GSATA) record through 1980 is impossibly less than the 2σ = ±0.25 °C lower limit of laboratory resolution of 1 °C/division liquid-in-glass (LiG) thermometers. The ~0.7 °C/century Joule-drift of lead- and soft-glass thermometer bulbs renders unreliable the entire historical air-temperature record through the 19th century. A circa 1900 Baudin meteorological spirit thermometer bulb exhibited intense Pb X-ray emission lines (10.55, 12.66, and 14.76 keV). Uncorrected LiG thermometer non-linearity leaves 1σ = ±0.27 °C uncertainty in land-surface air temperatures prior to 1981. The 2σ = ±0.43 °C from LiG resolution and non-linearity obscures most of the 20th century GSATA trend. Systematic sensor-measurement errors are highly pair-wise correlated, possibly across hundreds of km. Non-normal distributions of bucket and engine-intake difference SSTs disconfirm the assumption of random measurement error. Semivariogram analysis of ship SST measurements yields half the error difference mean, ±½Δε_1,2, not the error mean. Transfer-function adjustment following a change of land station air-temperature sensor eliminates measurement independence and forward-propagates the antecedent uncertainty. LiG resolution limits, non-linearity, and sensor field calibrations yield GSATA mean ±2σ RMS uncertainties of, 1900-1945, ±1.7 °C; 1946-1980, ±2.1 °C; 1981-2004, ±2.0 °C; and 2005-2010, ±1.6 °C. Finally, the 20th century (1900-1999) GSATA, 0.74 ± 1.94 °C, does not convey any information about rate or magnitude of temperature change.

Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snow cover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC =  - 0.74), and vegetation and snow cover (PCC =  - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher  5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.

Differences of energy adaptation strategies in Tupaia belangeri between Pianma and Tengchong region by metabolomics of liver: Role of warmer temperature

Global warming is becoming the future climate trend and will have a significant impact on small mammals, and they will also adapt at the physiological levels in response to climate change, among which the adaptation of energetics is the key to their survival. In order to investigate the physiological adaptation strategies in Tupaia belangeri affected by the climate change and to predict their possible fate under future global warming, we designed a metabonomic study in T. belangeri between two different places, including Pianma (PM, annual average temperature 15.01°C) and Tengchong (TC, annual average temperature 20.32°C), to analyze the differences of liver metabolite. Moreover, the changes of resting metabolic rate, body temperature, uncoupling protein 1content (UCP1) and other energy indicators in T. belangeri between the two places were also measured. The results showed that T. belangeri in warm areas (TC) reduced the concentrations of energy metabolites in the liver, such as pyruvic acid, fructose 6-phosphate, citric acid, malic acid, fumaric acid etc., so their energy metabolism intensity was also reduced, indicating that important energy metabolism pathway of glycolysis and tricarboxylic acid cycle (TCA) pathway reduced in T. belangeri from warmer habitat. Furthermore, brown adipose tissue (BAT) mass, UCP1 content and RMR in TC also decreased significantly, but their body temperature increased. All of the results suggested that T. belangeri adapt to the impact of warm temperature by reducing energy expenditure and increasing body temperature. In conclusion, our research had broadened our understanding of the physiological adaptation strategies to cope with climate change, and also provided a preliminary insight into the fate of T. belangeri for the future global warming climate.

Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880–2001 and 1880–2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time.

Co-variability of the summer NDVIs on the eastern Tibetan Plateau and in the Lake Baikal region: Associated climate factors and atmospheric circulation

The Tibetan Plateau and Siberia are both crucial regions in which the vegetation dynamics are sensitive to climate change. The variabilities in the normalized difference vegetation index (NDVI) over the two regions have been explored previously, but there have been few studies on the relationship of the NDVI in the two regions. Using the GIMMS-NDVI, GHCN-CAMS and NCEP reanalysis datasets and statistical and physical diagnostic methods, we show that the summer (June, July and August) NDVI over the eastern Tibetan Plateau and Lake Baikal and its adjacent eastern region of Siberia have an in-phase co-variability, especially on an interannual timescale (with a correlation coefficient of 0.69 during the time period 1982-2014). Further analyses show that precipitation and the related cloud cover and solar radiation are responsible for the variability in the NDVI over the eastern Tibetan Plateau, whereas temperature has the more important role in modulating the variability in the NDVI over the Lake Baikal region. A dipole pattern prevails over the Tibetan Plateau-Lake Baikal region and reflects the anomalies in the intensity and location of the South Asian high and the northeast Asian blocking high. This dipole pattern simultaneously modulates precipitation over the eastern Tibetan Plateau and the temperature over the Lake Baikal region and leads to the co-variability of the NDVI between the two regions. A synergistic sea surface temperature index, which reflects sea surface temperature anomalies in the eastern tropical Pacific Ocean, the northwest Pacific Ocean, the northern Indian Ocean and the subtropical north Atlantic Ocean, appears to adjust this Tibetan Plateau-Lake Baikal dipole pattern and is therefore closely related to the co-variability of the NDVI between the eastern Tibetan Plateau and the Lake Baikal region. Our results suggest that vegetation dynamics may not be only a local phenomenon in some areas, but are also likely to remotely link with variations in vegetation over other regions.

On the interchangeability of sea-surface and near-surface air temperature anomalies in climatologies

On global and hemispheric scales, sea-surface temperature (SST) anomalies are assumed to be good surrogates for near-surface marine air temperature (MAT) anomalies. In fact, global gridded temperature datasets commonly blend SST and near-surface air temperature anomalies to overcome the lack of geographically homogeneous and reliable MAT observations. Here, we show that SST and MAT anomalies differ regarding crucial statistical properties such as multiannual trends and probabilistic distributions of daily and monthly averages. We provide evidence of the lack of interchangeability from an array of moored buoys in the tropical Pacific Ocean. We identify statistically significant discrepancies between SST and MAT anomalies for single as well as groups of such buoys. Thus, caution is required when characterizing and interpreting MAT variability through SST observations, especially at shorter than decadal timescale.

Satellite-based decadal change assessments of pan-Arctic environments

Remote sensing can advance the work of the Circumpolar Biodiversity Monitoring Program through monitoring of satellite-derived terrestrial and marine physical and ecological variables. Standardized data facilitate an unbiased comparison across variables and environments. Using MODIS standard products of land surface temperature, percent snow covered area, NDVI, EVI, phenology, burned area, marine chlorophyll, CDOM, sea surface temperature, and marine primary productivity, significant trends were observed in almost all variables between 2000 and 2017. Analysis of seasonal data revealed significant breakpoints in temporal trends. Within the terrestrial environment, data showed significant increasing trends in land surface temperature and NDVI. In the marine environment, significant increasing trends were detected in primary productivity. Significantly earlier onset of green up date was observed in bioclimate subzones C&E and longer end of growing season in B&E. Terrestrial and marine parameters showed similar rates of change with unidirectional change in terrestrial and significant directional and magnitude shifts in marine.

Climate change and African trypanosomiasis vector populations in Zimbabwe's Zambezi Valley: A mathematical modelling study

BACKGROUND

Quantifying the effects of climate change on the entomological and epidemiological components of vector-borne diseases is an essential part of climate change research, but evidence for such effects remains scant, and predictions rely largely on extrapolation of statistical correlations. We aimed to develop a mechanistic model to test whether recent increases in temperature in the Mana Pools National Park of the Zambezi Valley of Zimbabwe could account for the simultaneous decline of tsetse flies, the vectors of human and animal trypanosomiasis.

METHODS AND FINDINGS

The model we developed incorporates the effects of temperature on mortality, larviposition, and emergence rates and is fitted to a 27-year time series of tsetse caught from cattle. These catches declined from an average of c. 50 flies per animal per afternoon in 1990 to c. 0.1 in 2017. Since 1975, mean daily temperatures have risen by c. 0.9°C and temperatures in the hottest month of November by c. 2°C. Although our model provided a good fit to the data, it cannot predict whether or when extinction will occur.

CONCLUSIONS

The model suggests that the increase in temperature may explain the observed collapse in tsetse abundance and provides a first step in linking temperature to trypanosomiasis risk. If the effect at Mana Pools extends across the whole of the Zambezi Valley, then transmission of trypanosomes is likely to have been greatly reduced in this warm low-lying region. Conversely, rising temperatures may have made some higher, cooler, parts of Zimbabwe more suitable for tsetse and led to the emergence of new disease foci.

Coldest Temperature Extreme Monotonically Increased and Hottest Extreme Oscillated over Northern Hemisphere Land during Last 114 Years

Most studies on global warming rely on global mean surface temperature, whose change is jointly determined by anthropogenic greenhouse gases (GHGs) and natural variability. This introduces a heated debate on whether there is a recent warming hiatus and what caused the hiatus. Here, we presented a novel method and applied it to a 5° × 5° grid of Northern Hemisphere land for the period 1900 to 2013. Our results show that the coldest 5% of minimum temperature anomalies (the coldest deviation) have increased monotonically by 0.22 °C/decade, which reflects well the elevated anthropogenic GHG effect. The warmest 5% of maximum temperature anomalies (the warmest deviation), however, display a significant oscillation following the Atlantic Multidecadal Oscillation (AMO), with a warming rate of 0.07 °C/decade from 1900 to 2013. The warmest (0.34 °C/decade) and coldest deviations (0.25 °C/decade) increased at much higher rates over the most recent decade than last century mean values, indicating the hiatus should not be interpreted as a general slowing of climate change. The significant oscillation of the warmest deviation provides an extension of previous study reporting no pause in the hottest temperature extremes since 1979, and first uncovers its increase from 1900 to 1939 and decrease from 1940 to 1969.

Robust mixture of experts modeling using the t distribution

Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification, and clustering. For regression and cluster analyses of continuous data, MoE usually uses normal experts following the Gaussian distribution. However, for a set of data containing a group or groups of observations with heavy tails or atypical observations, the use of normal experts is unsuitable and can unduly affect the fit of the MoE model. We introduce a robust MoE modeling using the t distribution. The proposed t MoE (TMoE) deals with these issues regarding heavy-tailed and noisy data. We develop a dedicated expectation-maximization (EM) algorithm to estimate the parameters of the proposed model by monotonically maximizing the observed data log-likelihood. We describe how the presented model can be used in prediction and in model-based clustering of regression data. The proposed model is validated on numerical experiments carried out on simulated data, which show the effectiveness and the robustness of the proposed model in terms of modeling non-linear regression functions as well as in model-based clustering. Then, it is applied to the real-world data of tone perception for musical data analysis, and the one of temperature anomalies for the analysis of climate change data. The obtained results show the usefulness of the TMoE model for practical applications.

Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink

The terrestrial biosphere is currently a strong carbon (C) sink but may switch to a source in the 21st century as climate-driven losses exceed CO2-driven C gains, thereby accelerating global warming. Although it has long been recognized that tropical climate plays a critical role in regulating interannual climate variability, the causal link between changes in temperature and precipitation and terrestrial processes remains uncertain. Here, we combine atmospheric mass balance, remote sensing-modeled datasets of vegetation C uptake, and climate datasets to characterize the temporal variability of the terrestrial C sink and determine the dominant climate drivers of this variability. We show that the interannual variability of global land C sink has grown by 50-100% over the past 50 y. We further find that interannual land C sink variability is most strongly linked to tropical nighttime warming, likely through respiration. This apparent sensitivity of respiration to nighttime temperatures, which are projected to increase faster than global average temperatures, suggests that C stored in tropical forests may be vulnerable to future warming.

Large carbon release legacy from bark beetle outbreaks across Western United States

Warmer conditions over the past two decades have contributed to rapid expansion of bark beetle outbreaks killing millions of trees over a large fraction of western United States (US) forests. These outbreaks reduce plant productivity by killing trees and transfer carbon from live to dead pools where carbon is slowly emitted to the atmosphere via heterotrophic respiration which subsequently feeds back to climate change. Recent studies have begun to examine the local impacts of bark beetle outbreaks in individual stands, but the full regional carbon consequences remain undocumented for the western US. In this study, we quantify the regional carbon impacts of the bark beetle outbreaks taking place in western US forests. The work relies on a combination of postdisturbance forest regrowth trajectories derived from forest inventory data and a process-based carbon cycle model tracking decomposition, as well as aerial detection survey (ADS) data documenting the regional extent and severity of recent outbreaks. We find that biomass killed by bark beetle attacks across beetle-affected areas in western US forests from 2000 to 2009 ranges from 5 to 15 Tg C yr(-1) and caused a reduction of net ecosystem productivity (NEP) of about 6.1-9.3 Tg C y(-1) by 2009. Uncertainties result largely from a lack of detailed surveys of the extent and severity of outbreaks, calling out a need for improved characterization across western US forests. The carbon flux legacy of 2000-2009 outbreaks will continue decades into the future (e.g., 2040-2060) as committed emissions from heterotrophic respiration of beetle-killed biomass are balanced by forest regrowth and accumulation.

Shortwave and longwave radiative contributions to global warming under increasing CO2

In response to increasing concentrations of atmospheric CO2, high-end general circulation models (GCMs) simulate an accumulation of energy at the top of the atmosphere not through a reduction in outgoing longwave radiation (OLR)—as one might expect from greenhouse gas forcing—but through an enhancement of net absorbed solar radiation (ASR). A simple linear radiative feedback framework is used to explain this counterintuitive behavior. It is found that the timescale over which OLR returns to its initial value after a CO2 perturbation depends sensitively on the magnitude of shortwave (SW) feedbacks. If SW feedbacks are sufficiently positive, OLR recovers within merely several decades, and any subsequent global energy accumulation is because of enhanced ASR only. In the GCM mean, this OLR recovery timescale is only 20 y because of robust SW water vapor and surface albedo feedbacks. However, a large spread in the net SW feedback across models (because of clouds) produces a range of OLR responses; in those few models with a weak SW feedback, OLR takes centuries to recover, and energy accumulation is dominated by reduced OLR. Observational constraints of radiative feedbacks—from satellite radiation and surface temperature data—suggest an OLR recovery timescale of decades or less, consistent with the majority of GCMs. Altogether, these results suggest that, although greenhouse gas forcing predominantly acts to reduce OLR, the resulting global warming is likely caused by enhanced ASR.

The ocean's role in polar climate change: asymmetric Arctic and Antarctic responses to greenhouse gas and ozone forcing

In recent decades, the Arctic has been warming and sea ice disappearing. By contrast, the Southern Ocean around Antarctica has been (mainly) cooling and sea-ice extent growing. We argue here that interhemispheric asymmetries in the mean ocean circulation, with sinking in the northern North Atlantic and upwelling around Antarctica, strongly influence the sea-surface temperature (SST) response to anthropogenic greenhouse gas (GHG) forcing, accelerating warming in the Arctic while delaying it in the Antarctic. Furthermore, while the amplitude of GHG forcing has been similar at the poles, significant ozone depletion only occurs over Antarctica. We suggest that the initial response of SST around Antarctica to ozone depletion is one of cooling and only later adds to the GHG-induced warming trend as upwelling of sub-surface warm water associated with stronger surface westerlies impacts surface properties. We organize our discussion around 'climate response functions' (CRFs), i.e. the response of the climate to 'step' changes in anthropogenic forcing in which GHG and/or ozone-hole forcing is abruptly turned on and the transient response of the climate revealed and studied. Convolutions of known or postulated GHG and ozone-hole forcing functions with their respective CRFs then yield the transient forced SST response (implied by linear response theory), providing a context for discussion of the differing warming/cooling trends in the Arctic and Antarctic. We speculate that the period through which we are now passing may be one in which the delayed warming of SST associated with GHG forcing around Antarctica is largely cancelled by the cooling effects associated with the ozone hole. By mid-century, however, ozone-hole effects may instead be adding to GHG warming around Antarctica but with diminished amplitude as the ozone hole heals. The Arctic, meanwhile, responding to GHG forcing but in a manner amplified by ocean heat transport, may continue to warm at an accelerating rate.

Increasing minimum daily temperatures are associated with enhanced pesticide use in cultivated soybean along a latitudinal gradient in the mid-western United States

Assessments of climate change and food security often do not consider changes to crop production as a function of altered pest pressures. Evaluation of potential changes may be difficult, in part, because management practices are routinely utilized in situ to minimize pest injury. If so, then such practices, should, in theory, also change with climate, although this has never been quantified. Chemical (pesticide) applications remain the primary means of managing pests in industrialized countries. While a wide range of climate variables can influence chemical use, minimum daily temperature (lowest 24 h recorded temperature in a given year) can be associated with the distribution and thermal survival of many agricultural pests in temperate regions. The current study quantifies average pesticide applications since 1999 for commercial soybean grown over a 2100 km North-South latitudinal transect for seven states that varied in minimum daily temperature (1999-2013) from -28.6°C (Minnesota) to -5.1°C (Louisiana). Although soybean yields (per hectare) did not vary by state, total pesticide applications (kg of active ingredient, ai, per hectare) increased from 4.3 to 6.5 over this temperature range. Significant correlations were observed between minimum daily temperatures and kg of ai for all pesticide classes. This suggested that minimum daily temperature could serve as a proxy for pesticide application. Longer term temperature data (1977-2013) indicated greater relative increases in minimum daily temperatures for northern relative to southern states. Using these longer-term trends to determine short-term projections of pesticide use (to 2023) showed a greater comparative increase in herbicide use for soybean in northern; but a greater increase in insecticide and fungicide use for southern states in a warmer climate. Overall, these data suggest that increases in pesticide application rates may be a means to maintain soybean production in response to rising minimum daily temperatures and potential increases in pest pressures.

Characterizing changes in soil bacterial community structure in response to short-term warming

High altitude alpine meadows are experiencing considerably greater than average increases in soil surface temperature, potentially as a result of ongoing climate change. The effects of warming on plant productivity and soil edaphic variables have been established previously, but the influence of warming on soil microbial community structure has not been well characterized. Here, the impact of 15 months of soil warming (both +1 and +2 °C) on bacterial community structure was examined in a field experiment on a Tibetan plateau alpine meadow using bar-coded pyrosequencing. Warming significantly changed (P < 0.05) the structure of the soil bacterial community, but the alpha diversity was not dramatically affected. Changes in the abundance of the Actinobacteria and Alphaproteobacteria were found to contribute the most to differences between ambient (AT) and artificially warmed conditions. A variance partitioning analysis (VPA) showed that warming directly explained 7.15% variation in bacterial community structure, while warming-induced changes in soil edaphic and plant phenotypic properties indirectly accounted for 28.3% and 20.6% of the community variance, respectively. Interestingly, certain taxa showed an inconsistent response to the two warming treatments, for example Deltaproteobacteria showed a decreased relative abundance at +1 °C, but a return to AT control relative abundance at +2 °C. This suggests complex microbial dynamics that could result from conditional dependencies between bacterial taxa.

Oxygen isotopes in tree rings record variation in precipitation δ18O and amount effects in the south of Mexico

[1] Natural archives of oxygen isotopes in precipitation may be used to study changes in the hydrological cycle in the tropics, but their interpretation is not straightforward. We studied to which degree tree rings of Mimosa acantholoba from southern Mexico record variation in isotopic composition of precipitation and which climatic processes influence oxygen isotopes in tree rings (δ¹⁸O_tr). Interannual variation in δ¹⁸O_tr was highly synchronized between trees and closely related to isotopic composition of rain measured at San Salvador, 710 km to the southwest. Correlations with δ¹³C, growth, or local climate variables (temperature, cloud cover, vapor pressure deficit (VPD)) were relatively low, indicating weak plant physiological influences. Interannual variation in δ¹⁸O_tr correlated negatively with local rainfall amount and intensity. Correlations with the amount of precipitation extended along a 1000 km long stretch of the Pacific Central American coast, probably as a result of organized storm systems uniformly affecting rainfall in the region and its isotope signal; episodic heavy precipitation events, of which some are related to cyclones, deposit strongly ¹⁸O-depleted rain in the region and seem to have affected the δ¹⁸O_tr signal. Large-scale controls on the isotope signature include variation in sea surface temperatures of tropical north Atlantic and Pacific Ocean. In conclusion, we show that δ¹⁸O_tr of M. acantholoba can be used as a proxy for source water δ¹⁸O and that interannual variation in δ¹⁸O_prec is caused by a regional amount effect. This contrasts with δ¹⁸O signatures at continental sites where cumulative rainout processes dominate and thus provide a proxy for precipitation integrated over a much larger scale. Our results confirm that processes influencing climate-isotope relations differ between sites located, e.g., in the western Amazon versus coastal Mexico, and that tree ring isotope records can help in disentangling the processes influencing precipitation δ¹⁸O.

Variations in atmospheric CO2 growth rates coupled with tropical temperature

Previous studies have highlighted the occurrence and intensity of El Niño-Southern Oscillation as important drivers of the interannual variability of the atmospheric CO2 growth rate, but the underlying biogeophysical mechanisms governing such connections remain unclear. Here we show a strong and persistent coupling (r(2) ≈ 0.50) between interannual variations of the CO2 growth rate and tropical land-surface air temperature during 1959 to 2011, with a 1 °C tropical temperature anomaly leading to a 3.5 ± 0.6 Petagrams of carbon per year (PgC/y) CO2 growth-rate anomaly on average. Analysis of simulation results from Dynamic Global Vegetation Models suggests that this temperature-CO2 coupling is contributed mainly by the additive responses of heterotrophic respiration (Rh) and net primary production (NPP) to temperature variations in tropical ecosystems. However, we find a weaker and less consistent (r(2) ≈ 0.25) interannual coupling between CO2 growth rate and tropical land precipitation than diagnosed from the Dynamic Global Vegetation Models, likely resulting from the subtractive responses of tropical Rh and NPP to precipitation anomalies that partly offset each other in the net ecosystem exchange (i.e., net ecosystem exchange ≈ Rh - NPP). Variations in other climate variables (e.g., large-scale cloudiness) and natural disturbances (e.g., volcanic eruptions) may induce transient reductions in the temperature-CO2 coupling, but the relationship is robust during the past 50 y and shows full recovery within a few years after any such major variability event. Therefore, it provides an important diagnostic tool for improved understanding of the contemporary and future global carbon cycle.

Mechanistic models for the spatial spread of species under climate change

Global climate change is a major threat to biodiversity. The most common methods for predicting the response of biodiversity to changing climate do not explicitly incorporate fundamental evolutionary and ecological processes that determine species responses to changing climate, such as reproduction, dispersal, and adaptation. We provide an overview of an emerging mechanistic spatial theory of species range shifts under climate change. This theoretical framework explicitly defines the ecological processes that contribute to species range shifts via biologically meaningful dispersal, reproductive, and climate envelope parameters. We present methods for estimating the parameters of the model with widely available species occurrence and abundance data and then apply these methods to empirical data for 12 North American butterfly species to illustrate the potential use of the theory for global change biology. The model predicts species persistence in light of current climate change and habitat loss. On average, we estimate that the climate envelopes of our study species are shifting north at a rate of 3.25 +/- 1.36 km/yr (mean +/- SD) and that our study species produce 3.46 +/- 1.39 (mean +/- SD) viable offspring per individual per year. Based on our parameter estimates, we are able to predict the relative risk of our 12 study species for lagging behind changing climate. This theoretical framework improves predictions of global change outcomes by facilitating the development and testing of hypotheses, providing mechanistic predictions of current and future range dynamics, and encouraging the adaptive integration of theory and data. The theory is ripe for future developments such as the incorporation of biotic interactions and evolution of adaptations to novel climatic conditions, and it has the potential to be a catalyst for the development of more effective conservation strategies to mitigate losses of biodiversity from global climate change.

REDUCTIONS OF NOISE AND UNCERTAINTY IN ANNUAL GLOBAL SURFACE TEMPERATURE ANOMALY DATA

Global climate variability is currently a topic of high scientific and public interest, with potential ramifications for the Earth's ecologic systems and policies governing world economy. Across the broad spectrum of global climate variability, the least well understood time scale is that of decade-to-century.1 The bases for investigating past changes across that period band are the records of annual mean Global Surface Temperature Anomaly (GSTA) time series, produced variously in many painstaking efforts.2–5 However, due to incipient instrument noise, the uneven distribution of sensors spatially and temporally, data gaps, land urbanization, and bias corrections to sea surface temperature, noise and uncertainty continue to exist in all data sets.1, 2, 6–8 Using the Empirical Mode Decomposition method as a filter, we can reduce this noise and uncertainty and produce a cleaner annual mean GSTA dataset. The noise in the climate dataset is thus reduced by one-third and the difference between the new and the commonly used, but unfiltered time series, ranges up to 0.1506°C, with a standard deviation up to 0.01974°C, and an overall mean difference of only 0.0001°C. Considering that the total increase of the global mean temperature over the last 150 years to be only around 0.6°C, we believe this difference of 0.1506°C is significant.

Increase of extreme events in a warming world

We develop a theoretical approach to quantify the effect of long-term trends on the expected number of extremes in generic time series, using analytical solutions and Monte Carlo simulations. We apply our method to study the effect of warming trends on heat records. We find that the number of record-breaking events increases approximately in proportion to the ratio of warming trend to short-term standard deviation. Short-term variability thus decreases the number of heat extremes, whereas a climatic warming increases it. For extremes exceeding a predefined threshold, the dependence on the warming trend is highly nonlinear. We further find that the sum of warm plus cold extremes increases with any climate change, whether warming or cooling. We estimate that climatic warming has increased the number of new global-mean temperature records expected in the last decade from 0.1 to 2.8. For July temperature in Moscow, we estimate that the local warming trend has increased the number of records expected in the past decade fivefold, which implies an approximate 80% probability that the 2010 July heat record would not have occurred without climate warming.

Does global warming increase establishment rates of invasive alien species? A centurial time series analysis

Background

The establishment rate of invasive alien insect species has been increasing worldwide during the past century. This trend has been widely attributed to increased rates of international trade and associated species introductions, but rarely linked to environmental change. To better understand and manage the bioinvasion process, it is crucial to understand the relationship between global warming and establishment rate of invasive alien species, especially for poikilothermic invaders such as insects.

Methodology/Principal Findings

We present data that demonstrate a significant positive relationship between the change in average annual surface air temperature and the establishment rate of invasive alien insects in mainland China during 1900–2005. This relationship was modeled by regression analysis, and indicated that a 1°C increase in average annual surface temperature in mainland China was associated with an increase in the establishment rate of invasive alien insects of about 0.5 species year⁻¹. The relationship between rising surface air temperature and increasing establishment rate remained significant even after accounting for increases in international trade during the period 1950–2005. Moreover, similar relationships were detected using additional data from the United Kingdom and the contiguous United States.

Conclusions/Significance

These findings suggest that the perceived increase in establishments of invasive alien insects can be explained only in part by an increase in introduction rate or propagule pressure. Besides increasing propagule pressure, global warming is another driver that could favor worldwide bioinvasions. Our study highlights the need to consider global warming when designing strategies and policies to deal with bioinvasions.

Vegetable oil sources in diets for freshwater angelfish (Pterophyllum scalare, Cichlidae): growth and thermal tolerance

The influence of fatty acid composition of the diets on the productive performance and on cold and heat tolerance of juvenile freshwater angelfish (Pterophyllum scalare), in three different phases, was studied. Phase I studied the productive performance of freshwater angelfish in a completely randomized experimental design with four treatments, canola, linseed, olive and soybean oils and four replicates during 50 days using 192 fish in 16 aquaria. Phase II studied the cold tolerance of juvenile freshwater angelfish using 72 juvenile freshwater angelfish, coming from phase I and maintained in 12 aquaria climatized chamber. The temperature was reduced 1ºC per day, until the observation of 100% fish mortality. Phase III, it was studied the heat tolerance of juvenile freshwater angelfish employing an identical procedure to phase II, but with a daily increase of 1ºC. Significant differences (P>0.05) were not observed for any parameters evaluated. Thus, it was concluded that the type of vegetable oil (canola, linseed, olive and soybean) used as a diet supplement did not affect the productive performance, nor the tolerance to cold and heat, of juvenile freshwater angelfish.

The hot summer of 2010: redrawing the temperature record map of Europe

The summer of 2010 was exceptionally warm in eastern Europe and large parts of Russia. We provide evidence that the anomalous 2010 warmth that caused adverse impacts exceeded the amplitude and spatial extent of the previous hottest summer of 2003. "Mega-heatwaves" such as the 2003 and 2010 events likely broke the 500-year-long seasonal temperature records over approximately 50% of Europe. According to regional multi-model experiments, the probability of a summer experiencing mega-heatwaves will increase by a factor of 5 to 10 within the next 40 years. However, the magnitude of the 2010 event was so extreme that despite this increase, the likelihood of an analog over the same region remains fairly low until the second half of the 21st century.

Temperature-controlled organic carbon mineralization in lake sediments

Peatlands, soils and the ocean floor are well-recognized as sites of organic carbon accumulation and represent important global carbon sinks. Although the annual burial of organic carbon in lakes and reservoirs exceeds that of ocean sediments, these inland waters are components of the global carbon cycle that receive only limited attention. Of the organic carbon that is being deposited onto the sediments, a certain proportion will be mineralized and the remainder will be buried over geological timescales. Here we assess the relationship between sediment organic carbon mineralization and temperature in a cross-system survey of boreal lakes in Sweden, and with input from a compilation of published data from a wide range of lakes that differ with respect to climate, productivity and organic carbon source. We find that the mineralization of organic carbon in lake sediments exhibits a strongly positive relationship with temperature, which suggests that warmer water temperatures lead to more mineralization and less organic carbon burial. Assuming that future organic carbon delivery to the lake sediments will be similar to that under present-day conditions, we estimate that temperature increases following the latest scenarios presented by the Intergovernmental Panel on Climate Change could result in a 4-27 per cent (0.9-6.4 Tg C yr(-1)) decrease in annual organic carbon burial in boreal lakes.

Highly specialized microbial diversity in hyper-arid polar desert

The McMurdo Dry Valleys in Antarctica are a cold hyperarid polar desert that present extreme challenges to life. Here, we report a culture-independent survey of multidomain microbial biodiversity in McKelvey Valley, a pristine example of the coldest desert on Earth. We demonstrate that life has adapted to form highly-specialized communities in distinct lithic niches occurring concomitantly within this terrain. Endoliths and chasmoliths in sandstone displayed greatest diversity, whereas soil was relatively depauperate and lacked a significant photoautotrophic component, apart from isolated islands of hypolithic cyanobacterial colonization on quartz rocks in soil contact. Communities supported previously unreported polar bacteria and fungi, but archaea were absent from all niches. Lithic community structure did not vary significantly on a landscape scale and stochastic moisture input due to snowmelt resulted in increases in colonization frequency without significantly affecting diversity. The findings show that biodiversity near the cold-arid limit for life is more complex than previously appreciated, but communities lack variability probably due to the high selective pressures of this extreme environment.