Changes In Temperature Variability Research Articles

Amplified warming of North American cold extremes linked to human-induced changes in temperature variability

How global warming is impacting winter cold extremes is uncertain. Previous work has found decreasing winter temperature variability over North America which suggests a reduction in frequency and intensity of cold extremes relative to mean changes. However, others argue that cold air outbreaks are becoming more likely because of Arctic-induced changes in atmospheric circulation. Here we show that cold extremes over North America have warmed substantially faster than the winter mean temperature since 1980. This amplified warming is linked to both decreasing variance and changes in higher moments of the temperature distributions. Climate model simulations with historical forcings robustly capture the observed trends in extremes and variability. A pattern-based detection and attribution analysis shows that the changes in variability are detectable in observations and can be attributed to human influence. Our results highlight that human emissions are warming North American extreme cold temperatures beyond only shifting the winter mean temperature.

Attribution of the unprecedented summer 2022 compound marine and terrestrial heatwave in the Northwest Pacific

In boreal summer (July–August) 2022, an unprecedented marine heatwave (MHW) occurred in the northwest Pacific Ocean (NWP), while a record-breaking terrestrial heatwave (THW) hit the Yangtze River Basin (YRB). The temperature anomalies caused by this compound MHW-THW event exceeded climatology by 2.5 standard deviations (SDs), breaking the historical record for nearly 100 years, with severe impacts on the ecosystems and social economy. To investigate the underlying causes, we explored the potential roles of anthropogenic forcing, atmospheric circulation, and ‘triple-dip’ La Niña on this compound event using the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations. Results indicate that the 2022-like compound MHW-THW event was extremely unlikely to happen without anthropogenic warming, and that such extreme heatwaves were governed by the climatic mean temperature rather than changes in temperature variability. Notably, the anticyclone circulation patterns and associated high-pressure systems (i.e. western North Pacific subtropical high (WNPSH) and South Asian high (SAH)) increase the probability of a 2022-like MHW-THW event by 3.7 times. However, the La Niña phase has no significant effect on the occurrence probability of such events. We further estimate that the 2022-like MHW-THW event will become 7.5 and 11.4 times more likely under the SSP3-7.0 scenario by the middle and end of the 21st century, respectively. This study demonstrates the contribution of anthropogenic climate change and natural variability to the occurrence of compound MHW-THW events and highlights the urgent need to build mitigation strategies for compound MHW-THW events.

The thermal breadth of temperate and tropical freshwater insects supports the climate variability hypothesis.

Climate change involves increases in mean temperature and changes in temperature variability at multiple temporal scales but research rarely considers these temporal scales. The climate variability hypothesis (CVH) provides a conceptual framework for exploring the potential effects of annual scale thermal variability across climatic zones. The CVH predicts ectotherms in temperate regions tolerate a wider range of temperatures than those in tropical regions in response to greater annual variability in temperate regions. However, various other aspects of thermal regimes (e.g. diel variability), organisms' size and taxonomic identity are also hypothesised to influence thermal tolerance. Indeed, high temperatures in the tropics have been proposed as constraining organisms' ability to tolerate a wide range of temperatures, implying that high annual maximum temperatures would be associated with tolerating a narrow range of temperatures. We measured thermal regimes and critical thermal limits (CTmax and CTmin) of freshwater insects in the orders Ephemeroptera (mayflies), Plecoptera (stoneflies) and Trichoptera (caddisflies) along elevation gradients in streams in temperate and tropical regions of eastern Australia and tested the CVH by determining which variables were most correlated with thermal breadth (T br = CTmax - CTmin). Consistent with the CVH, T br tended to increase with increasing annual temperature range. T br also increased with body size and T br was generally wider in Plecoptera than in Ephemeroptera or Trichoptera. We also find some support for a related hypothesis, the climate extreme hypothesis (CEH), particularly for predicting upper thermal limits. We found no evidence that higher annual maximum temperature constrained individuals' abilities to tolerate a wide range of temperatures. The support for the CVH we document suggests that temperate organisms may be able to tolerate wider ranges of temperatures than tropical organisms. There is an urgent need to investigate other aspects of thermal regimes, such as diel temperature cycling and minimum temperature.

Investigation of marine temperature changes across temporal and spatial Gradients: Providing a fundament for studies on the effects of warming on marine ecosystem function and biodiversity

A current critical issue in climate change studies is how temperature changes and shifts on different spatial and temporal scales can affect organisms in terms of trends, variability and frequency of extremes. In this paper, we analysed marine temperature data on different temporal and spatial scales. We related the sea surface temperature data from the Helgoland Roads Time Series, one of the most important and detailed long-term in situ marine ecological time series, to the Sylt Roads, North Sea, Germany, Europe, North Atlantic and Northern Hemisphere surface temperatures. All time series showed a distinct upwards shift in temperature in the late 1980s, early 1990s, with positive trends in overall for the period between 1962 and 2019 ranging from 1 to 2 °C over 57 years. We quantified changes in temperature variability by comparing the years before and after 1990, on both long-term and seasonal scales. At Helgoland and Sylt, an increase in the number of warmer days in summer and a decrease in extremely cold days in winter are the new characteristics of the temperature pattern after 1990; higher than expected temperatures now also occur earlier during the year. For these locations, we observed the highest trends overall, i.e. of around 0.3 °C/decade. The observed bimodal shape of the probability density functions, characterized by winter and summer modes, had become more heterogeneous, with the cold mode peak moving to higher values and the steepness to the peak increasing, which is a consequence of a decrease in extremely cold days. North Atlantic Oscillation (NAO) and Multidecadal Oscillation (AMO) large-scale phenomena had no significant correlations or, for the NAO, were limited to the winter season at the regional and local scales. The closest landmass (mainland Germany) temperature was highly correlated with the North Sea sites. Taken together, our results suggest that marine pelagic ecosystems and their species are subject to temperature shifts with similar patterns but with variations in magnitude at the different scales. Temperature is one of the main drivers of species diversity and distribution, and this manifests on different spatial and temporal scales depending on population growth, life stages, cycles and habitat. Accordingly, we here present the temperature changes on the appropriate spatio-temporal scales, and thus provide the suitable and useful fundament for studies on the effects of warming on marine ecosystem function and biodiversity.

Modeling the effects of present-day irrigation on temperature extremes over China

Irrigation activities have been recognized as one of the most potentially sustainable land management practices to tackle climate change. In this study, we investigated the impacts of present-day irrigation activities on regional temperature extremes changes in China using the state-of-the-art regional climate model RegCM-4.8.2, through two sets of long-term numerical experiments (i.e., with and without irrigation). The key findings are as follows. Firstly, by comparing the observations, RegCM4 has a good ability to reproduce both the spatial distribution and the volume of irrigation for provincial irrigation activities in China. In this context, by comparing the results of numerical experiments, we found that irrigation led to significant reductions (slightly changes) in the extreme indices associated with the warm (cold) tails of the maximum and minimum temperatures distributions, e.g., hot days, tropical nights (cold nights, frost days), particularly in the regions with intense irrigation (e.g., the North China Plain). Furthermore, these extreme indices were dominated by irrigation-induced shifts in mean temperatures, but the impacts of changes in temperature variability on extreme hot events were also non-negligible for the strongly irrigated regions. In addition, through attributing irrigation-induced changes in mean state temperatures, we revealed that the irrigation-induced repartitioning of the surface turbulent heat fluxes (i.e., sensible and latent heat) dominated the variations of temperatures and the associated extremes. However, region-specific land-atmosphere coupling features also led to differences in the irrigation-induced responses of surface energy components. We highlight the possible effects and mechanisms of irrigation on multiple types of extreme temperature indices, particularly the extreme hot events, which provide scientific clues for mitigating and adapting regional climate change.

Probing the timescale dependency of local and global variations in surface air temperature from climate simulations and reconstructions of the last millennia.

Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. Essential climate variables, such as surface air temperature, describe this dynamics. Our current interglacial, the Holocene (11700 yr ago to today), has been characterized by small variations in global mean temperature prior to anthropogenic warming. However, the mechanisms and spatiotemporal patterns of fluctuations around this mean, called temperature variability, are poorly understood despite their socioeconomic relevance for climate change mitigation and adaptation. Here we examine discrepancies between temperature variability from model simulations and paleoclimate reconstructions by categorizing the scaling behavior of local and global surface air temperature on the timescale of years to centuries. To this end, we contrast power spectral densities (PSD) and their power-law scaling using simulated and observation-based temperature series of the last 6000 yr. We further introduce the spectral gain to disentangle the externally forced and internally generated variability as a function of timescale. It is based on our estimate of the joint PSD of radiative forcing, which exhibits a scale break around the period of 7 yr. We find that local temperature series from paleoclimate reconstructions show a different scaling behavior than simulated ones, with a tendency towards stronger persistence (i.e., correlation between successive values within a time series) on periods of 10 to 200 yr. Conversely, the PSD and spectral gain of global mean temperature are consistent across data sets. Our results point to the limitation of climate models to fully represent local temperature statistics over decades to centuries. By highlighting the key characteristics of temperature variability, we pave a way to better constrain possible changes in temperature variability with global warming and assess future climate risks.

Observed Quantile Features of Summertime Temperature Trends over China: Non-Negligible Role of Temperature Variability

Changes in temperature variability can have more serious social and ecological impacts than changes in the mean state of temperature, especially when they are concurrent with global warming. The present study examines the summertime temperatures’ trends over China from the quantile perspective. Through fully investigating the quantile trends (QTs) of the maximum (Tmax) and minimum temperature (Tmin) using the homogenized observation data and quantile regression analysis, we identify evident region-specific quantile features of summertime temperature trends. In most of northern China, the QTs in Tmax and Tmin for all percentiles generally show strong uniform warmings, which are dominated by a warm shift in mean state temperatures. In contrast, the QTs of Tmax in the Yangtze River Basin show distinguishable inter-quantile features, i.e., an increasing tendency of QTs from cooling trends in the lower percentile to warming trends in the higher percentile. Further investigations show that such robust growing QTs of Tmax across quantiles are dominated by the temperature variance. Our results highlight that more attention should be paid to the region-specific dominance of temperature variability in trends and the related causes.

Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century

Global warming is expected to not only impact mean temperatures but also temperature variability, substantially altering climate extremes. Here we show that human-caused changes in internal year-to-year temperature variability are expected to emerge from the unforced range by the end of the 21st century across climate model initial-condition large ensembles forced with a strong global warming scenario. Different simulated changes in globally averaged regional temperature variability between models can be explained by a trade-off between strong increases in variability on tropical land and substantial decreases in high latitudes, both shown by most models. This latitudinal pattern of temperature variability change is consistent with loss of sea ice in high latitudes and changes in vegetation cover in the tropics. Instrumental records are broadly in line with this emerging pattern, but have data gaps in key regions. Paleoclimate proxy reconstructions support the simulated magnitude and distribution of temperature variability. Our findings strengthen the need for urgent mitigation to avoid unprecedented changes in temperature variability.

Projected near-term changes in temperature extremes over China in the mid-twenty-first century and underlying physical processes

This study investigates the near-term future changes of temperature extremes in summer (June–August) and winter (December–February) seasons over mainland China in the mid-twenty-first century (FP; 2045–2055) under representative concentration pathway (RCP) 4.5 scenario relative to the present day (PD; 1994–2011) by using an atmosphere–ocean-mixed-layer coupled model MetUM-GOML1. The projected changes in hot extremes exhibit a rise in hottest day temperature (TXx) and warmest night temperature (TNx) and an increase in frequencies of summer days (SU) and tropical nights (TR). The projected changes in cold extremes show a rise in coldest day temperature (TXn) and coldest night temperature (TNn) and a decrease in frequencies of ice days (ID) and frost days (FD). The projected changes in temperature extremes in both seasons are primarily determined by changes in seasonal mean daily maximum and minimum temperature while changes in temperature variability from daily to sub-seasonal time scales play a minor role. The future changes in temperature extremes over China, being consistent with the rise in seasonal temperature, are partly due to the increase in surface downward clear sky longwave radiation through the increased greenhouse gas concentrations and enhanced water vapor in the atmosphere, and partly due to the increase in net surface shortwave radiation as a result of the decreased aerosol emissions over Asia via aerosol-radiation interactions. Moreover, the seasonal mean surface warming can further be amplified with positive feedbacks by reducing the cloud cover, leading to positive changes in shortwave radiative effect through aerosol-cloud interactions and surface-atmosphere feedbacks during summer, and by positive changes in surface clear sky shortwave radiation through snow-albedo feedbacks over northern China and southwestern China during winter.

Temperature variability alters the stability and thresholds for collapse of interacting species.

Temperature variability and extremes can have profound impacts on populations and ecological communities. Predicting impacts of thermal variability poses a challenge, because it has both direct physiological effects and indirect effects through species interactions. In addition, differences in thermal performance between predators and prey and nonlinear averaging of temperature-dependent performance can result in complex and counterintuitive population dynamics in response to climate change. Yet the combined consequences of these effects remain underexplored. Here, modelling temperature-dependent predator-prey dynamics, we study how changes in temperature variability affect population size, collapse and stable coexistence of both predator and prey, relative to under constant environments or warming alone. We find that the effects of temperature variation on interacting species can lead to a diversity of outcomes, from predator collapse to stable coexistence, depending on interaction strengths and differences in species' thermal performance. Temperature variability also alters predictions about population collapse-in some cases allowing predators to persist for longer than predicted when considering warming alone, and in others accelerating collapse. To inform management responses that are robust to future climates with increasing temperature variability and extremes, we need to incorporate the consequences of temperature variation in complex ecosystems. This article is part of the theme issue 'Integrative research perspectives on marine conservation'.

Temperature trends and elevation dependent warming during 1965–2014 in headwaters of Yangtze River, Qinghai Tibetan Plateau

The understanding of temperature trends in high elevation mountain areas is an integral part of climate change research and it is critical for assessing the impacts of climate change on water resources including glacier melt, degradation of soils, and active layer thickness. In this study, climate changes were analyzed based on trends in air temperature variables (Tmax, Tmin, Tmean), and Diurnal Temperature Range (DTR) as well as elevation-dependent warming at annual and seasonal scales in the Headwaters of Yangtze River (HWYZ), Qinghai Tibetan Plateau. The Base Period (1965-2014) was split into two subperiods; Period-I (1965-1989) and Period-II (1990- 2014) and the analysis was constrained over two subbasins; Zhimenda and Tuotuohe. Increasing trends were found in absolute changes in temperature variables during Period-II as compared to Period-I. Tmax, Tmin, and Tmean had significant increasing trends for both sub-basins. The highest significant trends in annual time scale were observed in Tmin (1.15°C decade−1) in Tuotuohe and 0.98°C decade−1 in Zhimenda sub-basins. In Period-II, only the winter season had the highest magnitudes of Tmax and Tmin 0.58°C decade−1 and 1.26°C decade−1 in Tuotuohe subbasin, respectively. Elevation dependent warming analysis revealed that Tmax, Tmin and Tmean trend magnitudes increase with the increase of elevations in the middle reaches (4000 m to 4400 m) of the HWYZ during Period-II annually. The increasing trend magnitude during Period-II, for Tmax, is 1.77, 0.92, and 1.31°C decade−1, for Tmin 1.20, 1.32 and 1.59°C decade−1, for Tmean 1.51, 1.10 and 1.51°C decade−1 at elevations of 4066 m, 4175 m and 4415 m respectively in the winter season. Tmean increases during the spring season for > 3681 m elevations during Period-II, with no particular relation with elevation dependency for other variables. During the summer season in Period- II, Tmax, Tmin, Tmean increases with the increase of elevations (3681 m to 4415 m) in the middle reaches of HWYZ. Elevation dependent warming (EDW), the study concluded that magnitudes of Tmin are increasing significantly after the 1990s as compared to Tmax in the HWYZ. It is concluded that the climate of the HWYZ is getting warmer in both sub-basins and the rate of warming was more evident after the 1990s. The outcomes of the study provide an essential insight into climate change in the region and would be a primary index to select and design research scenarios to explore the impacts of climate change on water resources.

Projected near-term changes in three types of heat waves over China under RCP4.5

The changes in three aspects of frequency, intensity and duration of the compound, daytime and nighttime heat waves (HWs) over China during extended summer (May–September) in a future period of the mid-21st century (FP; 2045–2055) under RCP4.5 scenario relative to present day (PD; 1994–2011) are investigated by two models, MetUM-GOML1 and MetUM-GOML2, which comprise the atmospheric components of two state-of-the-art climate models coupled to a multi-level mixed-layer ocean model. The results show that in the mid-21st century all three types of HWs in China will occur more frequently with strengthened intensity and elongated duration relative to the PD. The compound HWs will change most dramatically, with the frequency in the FP being 4–5 times that in the PD, and the intensity and duration doubling those in the PD. The changes in daytime and nighttime HWs are also remarkable, with the changes of nighttime HWs larger than those of daytime HWs. The future changes of the three types of HWs in China in two models are similar in terms of spatial patterns and area-averaged quantities, indicating these projected changes of HWs over the China under RCP4.5 scenario are robust. Further analyses suggest that projected future changes in HWs over China are determined mainly by the increase in seasonal mean surface air temperatures with change in temperature variability playing a minor role. The seasonal mean temperature increase is due to the increase in surface downward longwave radiation and surface shortwave radiation. The increase in downward longwave radiation results from the enhanced greenhouse effect and increased water vapour in the atmosphere. The increase in surface shortwave radiation is the result of the decreased aerosol emissions, via direct aerosol–radiation interaction and indirect aerosol–cloud interaction over southeastern and northeastern China, and the reduced cloud cover related to a decrease in relative humidity.

Impacts of Anthropogenic Forcings and El Niño on Chinese Extreme Temperatures

This study investigates the potential influences of anthropogenic forcings and natural variability on the risk of summer extreme temperatures over China. We use three multi-thousand-member ensemble simulations with different forcings (with or without anthropogenic greenhouse gases and aerosol emissions) to evaluate the human impact, and with sea surface temperature patterns from three different years around the El Niño–Southern Oscillation (ENSO) 2015/16 event (years 2014, 2015 and 2016) to evaluate the impact of natural variability. A generalized extreme value (GEV) distribution is used to fit the ensemble results. Based on these model results, we find that, during the peak of ENSO (2015), daytime extreme temperatures are smaller over the central China region compared to a normal year (2014). During 2016, the risk of nighttime extreme temperatures is largely increased over the eastern coastal region. Both anomalies are of the same magnitude as the anthropogenic influence. Thus, ENSO can amplify or counterbalance (at a regional and annual scale) anthropogenic effects on extreme summer temperatures over China. Changes are mainly due to changes in the GEV location parameter. Thus, anomalies are due to a shift in the distributions and not to a change in temperature variability.

Internal variability in European summer temperatures at 1.5 °C and 2 °C of global warming

We use the 100-member Grand Ensemble with the climate model MPI-ESM to evaluate the controllability of mean and extreme European summer temperatures with the global mean temperature targets in the Paris Agreement. We find that European summer temperatures at 2 °C of global warming are on average 1 °C higher than at 1.5 °C of global warming with respect to pre-industrial levels. In a 2 °C warmer world, one out of every two European summer months would be warmer than ever observed in our current climate. Daily maximum temperature anomalies for extreme events with return periods of up to 500 years reach return levels of 7 °C at 2 °C of global warming and 5.5 °C at 1.5 °C of global warming. The largest differences in return levels for shorter return periods of 20 years are over southern Europe, where we find the highest mean temperature increase. In contrast, for events with return periods of over 100 years these differences are largest over central Europe, where we find the largest changes in temperature variability. However, due to the large effect of internal variability, only four out of every ten summer months in a 2 °C warmer world present mean temperatures that could be distinguishable from those in a 1.5 °C world. The distinguishability between the two climates is largest over southern Europe, while decreasing to around 10% distinguishable months over eastern Europe. Furthermore, we find that 10% of the most extreme and severe summer maximum temperatures in a 2 °C world could be avoided by limiting global warming to 1.5 °C.

The Sensitivity of Daily Temperature Variability and Extremes to Dataset Choice

AbstractRobust conclusions regarding changes in the temperature distribution rely on the accuracy and reliability of the input datasets used. Differences between methodologies and datasets in previous studies add uncertainty when comparing and quantifying findings. Here, the authors investigate the sensitivity of assessing global and regional temperature variability and extremes over 1980–2014 in gridded datasets of daily temperature anomalies. A gridded in situ–based dataset, Hadley Centre Global Historical Climatology Network–Daily (HadGHCND), is compared against several commonly used reanalysis products by assessing both the entire distribution and the tails of the distribution. Empirical probability distribution functions show sensitivity to the input dataset when estimating aspects such as standard deviation and skewness, with the mean showing robust results for most regions, irrespective of dataset choice. Standard deviation is especially sensitive, with larger disagreements between datasets for some regions more than others, such as Africa and the Mediterranean region, and with larger differences in minimum temperatures compared with maximum temperatures. Estimates of extreme parameters also show sensitivity to dataset choice, particularly in the lower tails and for daily minimum temperature anomalies. Comparing changes in the means and the extremes of the temperature distributions, the cold extremes in the lower tails have been warming at a faster rate than the mean of the entire distribution for much of the Northern Hemisphere extratropics, with warm extremes warming at a faster rate than the mean in some subtropical regions. These documented sensitivities call for caution when assessing changes in temperature variability and extremes, as dataset choice can have substantial effects on results.

Spatiotemporal Hydroclimate Variability in Finland: Past Trends

Abstract Over the past decades, Finland has experienced changes in its climate: temperature and precipitation have increased, resulting in varying runoff patterns. These trends are well studied, but the changes in interannual variability are less known, despite their importance for understanding climate change. This research aims to assess spatiotemporal changes in variability of temperature, precipitation, and runoff for 1962–2014 at the subbasin scale in Finland. Temporal changes in variability were analyzed by constructing moving-window median absolute deviation time series at annual and seasonal scales. Subbasins with similar patterns of temporal variability were identified using principal component analysis and agglomerative hierarchical clustering. Presence of monotonic trends in variability was tested. Distinct areas with similar patterns of statistically significant changes in variability were found. Decreases in annual, winter, and summer temperature variability were discovered across Finland, in southern Finland, and in northern Finland, respectively. Precipitation variability increased in autumn in northern Finland. It also decreased in winter and spring in northern and central parts of Finland. Runoff variability increased in winter in most parts of Finland and in summer in the central parts, but decreased in spring in southern Finland. Comparison with existing studies illustrates that trends in mean climate and its variability do not necessarily match, highlighting the importance of addressing both aspects. The findings of this study provide new information on hydroclimatic variability in Nordic conditions and improve the possibility to adapt and predict the changes in hydroclimatic conditions, including weather extremes.

Emergent Constraints in Climate Projections: A Case Study of Changes in High-Latitude Temperature Variability

Abstract Climate projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensemble show a decrease in interannual surface temperature variability over high latitudes with a large intermodel spread, in particular over the areas of sea ice retreat. Here relationships are found between the models’ present-day performance in sea ice–related metrics and future changes in temperature variability. These relations, so-called emergent constraints, can produce ensembles of models calibrated with present-day observations with a narrower spread across their members than across the full ensemble. The underlying assumption is that models in better agreement with observations or reanalyses in a carefully selected metric probably have a more realistic representation of local processes, and therefore are more reliable for projections. Thus, the reliability of this method depends on the availability of high-quality observations or reanalyses. This work represents a step toward formalization of the emergent constraints framework, as so far there is no consensus on how the constraints should be best implemented. The authors quantify the reduction in spread from emerging constraints for various metrics and their combinations, different emission scenarios, and seasons. Some of the general features of emerging constraints are discussed, and how to effectively aggregate information across metrics and seasons to achieve the largest reduction in model spread. It is demonstrated, based on the case of temperature variability, that a robust constraint can be obtained by combining relevant metrics across all seasons. Such a constraint results in a strongly reduced spread across model projections, which is consistent with a process understanding of variability changes due to sea ice retreat.

Seasonal mean temperature changes control future heat waves

AbstractIncreased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes.

Increased variance in temperature and lag effects alter phenological responses to rapid warming in a subarctic plant community.

Summer temperature on the Cape Churchill Peninsula (Manitoba, Canada) has increased rapidly over the past 75years, and flowering phenology of the plant community is advanced in years with warmer temperatures (higher cumulative growing degree days). Despite this, there has been no overall shift in flowering phenology over this period. However, climate change has also resulted in increased interannual variation in temperature; if relationships between phenology and temperature are not linear, an increase in temperature variance may interact with an increase in the mean to alter how community phenology changes over time. In our system, the relationship between phenology and temperature was log-linear, resulting in a steeper slope at the cold end of the temperature spectrum than at the warm end. Because below-average temperatures had a greater impact on phenology than above-average temperatures, the long-term advance in phenology was reduced. In addition, flowering phenology in a given year was delayed if summer temperatures were high the previous year or 2 years earlier (lag effects), further reducing the expected advance over time. Phenology of early-flowering plants was negatively affected only by temperatures in the previous year, and that of late-flowering plants primarily by temperatures 2 years earlier. Subarctic plants develop leaf primordia one or more years prior to flowering (preformation); these results suggest that temperature affects the development of flower primordia during this preformation period. Together, increased variance in temperature and lag effects interacted with a changing mean to reduce the expected phenological advance by 94%, a magnitude large enough to account for our inability to detect a significant advance over time. We conclude that changes in temperature variability and lag effects can alter trends in plant responses to a warming climate and that predictions for changes in plant phenology under future warming scenarios should incorporate such effects.

Robust Future Changes in Temperature Variability under Greenhouse Gas Forcing and the Relationship with Thermal Advection

Abstract Recent temperature extremes have highlighted the importance of assessing projected changes in the variability of temperature as well as the mean. A large fraction of present-day temperature variance is associated with thermal advection, as anomalous winds blow across the land–sea temperature contrast, for instance. Models project robust heterogeneity in the twenty-first-century warming pattern under greenhouse gas forcing, resulting in land–sea temperature contrasts increasing in summer and decreasing in winter and the pole-to-equator temperature gradient weakening in winter. In this study, future changes in monthly variability of near-surface temperature in the 17-member ensemble ESSENCE (Ensemble Simulations of Extreme Weather Events under Nonlinear Climate Change) are assessed. In winter, variability in midlatitudes decreases whereas in very high latitudes and the tropics it increases. In summer, variability increases over most land areas and in the tropics, with decreasing variability in high latitude oceans. Multiple regression analysis is used to determine the contributions to variability changes from changing temperature gradients and circulation patterns. Thermal advection is found to be of particular importance in the Northern Hemisphere winter midlatitudes, where the change in mean state temperature gradients alone could account for over half the projected changes. Changes in thermal advection are also found to be important in summer in Europe and coastal areas, although less so than in winter. Comparison with CMIP5 data shows that the midlatitude changes in variability are robust across large regions, particularly high northern latitudes in winter and middle northern latitudes in summer.