Large Climate Variability Research Articles

Anthropogenic Fingerprint Detectable in Upper Tropospheric Ozone Trends Retrieved from Satellite.

Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability. We show that the anthropogenic signal ("fingerprint") in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño-Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe. Our analysis reveals a significant human effect on Earth's atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

Large Fires and Climatic Variability in Urban Europe, 1500–1800

Several hundred large urban fires occurred in Europe during the early modern period, but they did not take place randomly. This article charts their incidence and reveals a peak in the seventeenth century, coinciding with some of the coolest periods of the Little Ice Age. This apparent paradox can best be explained by climatic variability, since overall cooling was accompanied by numerous warm, dry anomalies. While the cause of fires was usually human activity, and small fires happened frequently, this paper shows that many of the largest conflagrations of the early modern period took place in years of such hot and/or dry climatic anomalies, and closer analysis of individual fires confirms that these meteorological conditions facilitated their spread. This strongly suggests that climatic variability associated with the Little Ice Age was a major determinant of the timing of large fires in Europe. Over the same broad period, climatic disasters linked to cooler and damper conditions contributed to social and political instability, and there is evidence that this in turn undermined fire prevention and control and thus further increased the likelihood of small fires becoming large ones.

The foraging ecology of yellow-billed and red- billed choughs changed between two climatically different years

Climate change is affecting the alpine ecosystem at an unprecedented rate, with marked changes in spring phenology and the elevation distribution of birds. Changes in the European Alps are happening rapidly, and it is possible behaviours stand to change from one year to the next. The year 2022 was characterised by climatic extremes: Italy experienced its hottest year ever, and it was the driest since 1800. Here, we assessed whether the foraging ecology of two coexisting upland bird species, the yellow-billed and the red-billed chough, changed from 2021 to 2022. We assessed foraging stay times, flock size, propensity to mixed flocking, foraging home ranges and altitudinal distribution. Stay times of both species when foraging in monospecific flocks significantly shortened in 2022, especially in the case of the red-billed chough. The two corvids are known to influence each other when foraging together. In 2021, as expected, the stay times of the red-billed chough decreased when in the presence of the congener, but this did not occur in 2022. Instead, the yellow-billed chough increased its altitudinal foraging distribution in 2022. The results are in line with the hypothesis that large climate variations may disrupt the foraging ecology of mountain birds. However, as it is not possible to draw solid conclusions from just two years of observations, further field research will have to be planned in the future.

Exploring site-specific N application rate to reduce N footprint and increase crop production for green manure-rice rotation system in southern China

Milk vetch (Astragalus sinicus L.) is leguminous green manure (GM) which produces organic nitrogen (N) for subsequent crops and is widely planted and utilized to simultaneously reduce the use of synthetic N fertilizer and its environmental costs in rice systems. Determination of an optimal N application rate specific to the GM-rice system is challenging because of the large temporal and spatial variations in soil, climate, and field management conditions. To solve this problem, we developed a framework to explore the site-specific N application rate for the low-N footprint rice production system in southern China based on multi-site field experiments, farmer field survey, and process-based model (WHCNS_Rice, soil water heat carbon nitrogen simulator for rice). The results showed that a process-based model can explain >83.3% (p < 0.01) of the variation in rice yield, aboveground biomass, crop N uptake, and soil mineral N. Based on the scenario analysis of the tested WHCNS_Rice model, the simple regression equation was developed to implement site-specific N application rates that considered variations in GM biomass, soil, and climatic conditions. Simulation evaluation on nine provinces in southern China showed that the site-specific N application rate reduced regional synthetic N fertilizer input by 29.6 ± 17.8% and 65.3 ± 23.0% for single and early rice, respectively; decreased their total N footprints (NFs) by 23.4% and 49.3%, respectively; and without reduction in rice yield, compared with traditional farming N practices. The reduction in total NF was attributed to the reduced emissions from ammonia volatilization by 35.2%, N leaching by 28.4%, and N runoff by 32.7%. In this study, we suggested a low NF rice production system that can be obtained by combining GM with site-specific N application rate in southern China.

Spatial and temporal variation of Fagus sylvatica growth in marginal areas under progressive climate change

The escalating decline in growth trends of European beech (Fagus sylvatica) observed across its distribution area pose a major ecological and economic challenge for countries with a high proportion of beech, such as Slovenia. In this study, the effects of climate change were examined at a high-resolution scale, encompassing the large climatic, orographic, and ecological variability of beech forests in Slovenia. Using basal area increment data (BAI) from a tree-ring network (48 sites in Slovenia), modelled climate data, and generalized linear mixed models (GLMM), we found an average growth decline of 11% between the 1953–1985 and 1986–2018 subperiods, affecting 90.5% of the forest stands. Based on climate data, we defined two contrasted marginal areas of beech presence (warm and cold) and analysed the growth changes over time. The warm marginal areas, which predominate near the geographical margin of beech distribution with a sub-Mediterranean climatic regime, were most affected by growth decline, threatening the survival of beech populations in the area. In contrast, cold marginal areas, mainly at high elevations in the Alps, where beech growth had previously been limited by low temperatures, turned out to be the only ones where growth of beech increased under prevailing warming conditions. Consequently, high elevation regions harbour climatic potential for increased beech growth performance, and may represent areas of future expansion of the species.

Wave runup and inundation dynamics on a perched beach

Sandy beaches perched over rocky shore platforms are common globally, yet their mixed sand and rocky morphology present challenges for quantifying and predicting wave runup and inundation. For typical linear beach profiles, simple relationships can be made between vertical runup and horizontal inundation based on beach slope. However, as topographic irregularities increase, substantial deviations from these relationships can occur. Shore platforms often experience a range of beach states, from full sand coverage (accreted) to complete shore platform erosion (exposed). As a result, existing methods for quantifying runup and inundation on purely sandy beaches are generally not directly applicable to these coastlines. To advance our understanding of runup and inundation on perched beaches, the aim of this work is three-fold: (1) to provide a method for quantifying beach slope and runup across a range of perched beach profiles, (2) to assess the relationship between vertical runup and horizontal inundation observations, and (3) to understand how different hydrodynamic mechanisms (i.e., setup, swash) contribute to runup during different beach states. To achieve this, we conducted an 8-month field study along a perched beach in southwestern Australia that experiences large seasonal variations in beach state and wave climate. A method was developed to measure runup and beach slope when only the topography shoreward of the shore platform edge was known. Using this method, an approximately linear relationship between inundation and runup was identified by incorporating beach slope. Our observations suggest that the components that dominate runup on perched beaches were primarily dependent on beach state (i.e., accreted versus exposed). Runup was dominated by swash in the infragravity band when in an accreted beach state, and setup when in an exposed beach state. These results aid our understanding of coastal processes on perched beaches, while providing new methods applicable to a range of perched beach profiles.

Increased primary mineral dissolution control on a terrestrial silicate lithium isotope record during the middle Miocene Climate Optimum

Lithium isotopes have great potential to fractionate at subaerial environment, so they are promising proxies for tracing chemical weathering history. However, it remains uncertain how to relate lithium isotopic compositions (δ7Li) in sediments to silicate weathering and past climate in terrestrial setting. To tackle this question, we choose the warm and wet middle Miocene climate optimum (MMCO, 17–14 Ma) with large climate variation amplitude and the following cold and dry middle Miocene climate cooling period (MMCC, 14–11 Ma) with damped climatic variation amplitude, as two climate contrasting intervals to study lithium isotopes and content ([Li]) variations of terrestrial mudrocks. Our study site is located at the eastern Qaidam Basin of the northeastern Tibetan Plateau and the precipitation pattern of this part of the basin is similar to the Chinese Loess Plateau over the two studied intervals. The study site is suitable for clarifying link between climate and δ7Li variations because previous studies have demonstrated no provenance shift over the studied interval and short transport distance from source to the study site suggests little chemical weathering over the transportation process. Both δ7Li and [Li] values show larger amplitude over the MMCO (spanning from −1.2 to 3.2 ‰ and from 32.8 to 93.9 ug/g, respectively) than the MMCC interval (−1.8 to 1.2 ‰ and 41.7–80.8 ug/g, respectively), consistent with general feature of global and Chinese Loess Plateau climate. However, δ7Li data are higher over the MMCO in comparison with the MMCC, contrary to expectation of more lithium isotope fractionation at warm interval. We interpret the more positive δ7Li values over the MMCO using increased ratio of primary mineral dissolution over secondary mineral precipitation. Qaidam mudrock and benthic Li records show complementary changes, suggesting their possible link. Furthermore, past research reveals lack of focused uplift of the northeastern Tibetan Plateau over the studied 18–11 Ma. So we conclude that global climate, rather than Tibetan uplift, played an important role in modulating inland Qaidam Basin climate variations over the middle Miocene and that increased chemical weathering over warm climate can lead to a small magnitude of lithium isotope fractionation. Therefore, interpreting chemical weathering history, and hence climate, using lithium isotope data in terrestrial setting requires considering relative importance of primary mineral dissolution versus secondary mineral precipitation.

Climate Responses Under an Extreme Quiet Sun Scenario

AbstractFundamental understanding of the climate responses to solar variability is obscured by the large and complex climate variability. This long‐standing issue is addressed here by examining climate responses under an extreme quiet sun (EQS) scenario, obtained by making the sun void of all magnetic fields. It is used to drive a coupled climate model with whole atmosphere and ocean components. The simulations reveal significant responses, and elucidate aspects of the responses to changes of troposphere/surface forcing and stratospheric forcing that are similar and those that are different. Planetary waves (PWs) play a key role in both regional‐scale responses and the mean circulation changes. Intermediate scale stationary waves and regional climate respond to solar forcing changes in the troposphere and stratosphere in a similar way, due to similar subtropical wind changes in the upper troposphere. The patterns of these changes are similar to those found in a warming climate, but with opposite signs. Responses of the largest scale PW during northern hemisphere and southern hemisphere winters differ, leading to hemispheric differences in the interplay between dynamical and radiative processes. The analysis exposes remarkable general similarities between climate responses in EQS simulations and those under nominal solar minimum conditions, even though the latter may not always appear to be statistically significant.

Large variations in afforestation-related climate cooling and warming effects across short distances

Climate-related benefits of afforestation depend on the balance of the often-contrasting effects of biogeochemical (carbon sequestration) and biogeophysical (radiation balance) effects. These effects are known to vary at the continental scale (e.g., from boreal to tropical regions). Here, we show in a four-year study that the biogeochemical vs. biogeophysical balance in paired forested and non-forested ecosystems across short distances (approximately 200 Km) and steep aridity gradient (aridity index 0.64 to 0.18) can change dramatically. The required time for the forestation cooling effects via carbon sequestration, to surpass warming effects associated with the forests’ reduced albedo and suppressed longwave radiation, decreased from 213 years in the driest sites to 73 years in the intermediate and 43 years in the wettest sites. Climate-related benefits of forestation, previously considered at large-spatial scales, should be considered at high-spatial resolutions in climate-change mitigation programs aimed at taking advantage of the vast non-forested dry regions.

Reconstruction of Zonal Precipitation From Sparse Historical Observations Using Climate Model Information and Statistical Learning

Future projected changes in precipitation substantially impact societies worldwide. However, large uncertainties remain due to sparse historical observational coverage, large internal climate variability, and climate model disagreement. Here, we present a novel reconstruction of seasonally averaged zonal precipitation metrics from sparse rain-gauge data using regularized regression techniques that are trained across climate model simulations. Subsequently, we test the reconstruction on independent satellite data and reanalyzed precipitation, and find a large fraction of historical zonal mean precipitation (ZMP) variability is recovered, in particular over the Northern hemisphere and in parts of the tropics. Finally, we demonstrate that the reconstructed ZMP trends are outside the variability of pre-industrial control simulations, and are largely consistent with the range of historical simulations driven by external forcing. Overall, we illustrate a novel way of estimating seasonally averaged zonal precipitation from gauge data, and trends therein that show a signal very likely caused by human influence.

Uncertainty in high‐resolution hydrological projections: Partitioning the influence of climate models and natural climate variability

AbstractA major challenge in assessing the impacts of climate change on hydrological processes lies in dealing with large degrees of uncertainty in the future climate projections. Part of the uncertainty is owed to the intrinsic randomness of climate phenomena, which is considered irreducible. Additionally, modelling the response of hydrological processes to the changing climate requires the use of a chain of numerical models, each of which contributes some degree of uncertainty to the final outputs. As a result, hydrological projections, despite the progressive increase in the accuracy of the models along the chain, still display high levels of uncertainty, especially at small temporal and spatial scales. In this work, we present a framework to quantify and partition the uncertainty of hydrological processes emerging from climate models and internal variability, across a broad range of scales. Using the example of two mountainous catchments in Switzerland, we produced high‐resolution ensembles of climate and hydrological data using a two‐dimensional weather generator (AWE‐GEN‐ 2d) and a distributed hydrological model (TOPKAPI‐ETH). We quantified the uncertainty in hydrological projections towards the end of the century through the estimation of the values of signal‐to‐noise ratios (STNR). We found small STNR absolute values (&lt;1) in the projection of annual streamflow for most sub‐catchments in both study sites that are dominated by the large natural variability of precipitation (explains ~70% of total uncertainty). Furthermore, we investigated in detail specific hydrological components that are critical in the model chain. For example, snowmelt and liquid precipitation exhibit robust change signals, which translates into high STNR values for streamflow during warm seasons and at higher elevations, together with a larger contribution of climate model uncertainty. In contrast, projections of extreme high flows show low STNR values due to large internal climate variability across all elevations, which limits the potential for narrowing their estimation uncertainty.

Soil fungal communities vary more with soil characteristics than tree diversity at a local scale

Soil fungal communities vary spatially due to factors including variations in plant diversity and soil characteristics; however, the relative influences of these factors on composition and therefore function remain unclear. Small-scale variation in fungal communities may drive local variation in nutrient cycling and decomposition and may respond more to local factors compared with large climatic variations. Clarifying the roles of these factors can improve our predictions of soil fungal community and biogeochemical cycling responses to anthropogenic changes. Therefore, we examined relationships among abiotic and biotic factors and soil fungal communities associated with sapling and mature trees in a mixed-hardwood woodland. We also compared community composition and fungal enzymatic activity. Fungal community composition was most associated with spatial heterogeneity of soil characteristics, while sapling and mature tree species identity were poor predictors of community composition. Further, most of the compositional variation was unexplained by measured variables, suggesting stochasticity and other environmental characteristics may drive spatial variation in these communities. Additionally, enzymatic activity did not clearly correlate with fungal community composition. Overall, soil fungal communities and enzymatic activity adjacent to trees in this woodland are most likely influenced by soil characteristics and not plant species identity.

Global forests are influenced by the legacies of past inter-annual temperature variability

Inter-annual climate variability (hereafter climate variability) is increasing in many forested regions due to climate change. This variability could have larger near-term impacts on forests than decadal shifts in mean climate, but how forests will respond remains poorly resolved, particularly at broad scales. Individual trees, and even forest communities, often have traits and ecological strategies—the legacies of exposure to past variable conditions—that confer tolerance to subsequent climate variability. However, whether local legacies also shape global forest responses is unknown. Our objective was to assess how past and current climate variability influences global forest productivity. We hypothesized that forests exposed to large climate variability in the past would better tolerate current climate variability than forests for which past climate was relatively stable. We used historical (1950–1969) and contemporary (2000–2019) temperature, precipitation, and vapor pressure deficit (VPD) and the remotely sensed enhanced vegetation index (EVI) to quantify how historical and contemporary climate variability relate to patterns of contemporary forest productivity. Consistent with our hypothesis, forests exposed to large temperature variability in the past were more tolerant of contemporary temperature variability than forests where past temperatures were less variable. Forests were 19-fold times less sensitive to contemporary temperature variability where historical inter-annual temperature variability was 0.66 °C (two standard deviations) greater than the global average historical temperature variability. We also found that larger increases in temperature variability between the two study periods often eroded the tolerance conferred by the legacy effects of historical temperature variability. However, the hypothesis was not supported in the case of precipitation and VPD variability, potentially due to physiological tradeoffs inherent in how trees cope with dry conditions. We conclude that the sensitivity of forest productivity to imminent increases in temperature variability may be partially predictable based on the legacies of past conditions.

Effects of Internal Climate Variability on Historical Ocean Wave Height Trend Assessment

This study assesses the effects of internal climate variability on wave height trend assessment using the d4PDF-WaveHs, the first single model initial-condition large ensemble (100-member) of significant wave height (Hs) simulations for the 1951–2010 period, which was produced using sea level pressure taken from Japan’s d4PDF ensemble of historical climate simulations. Here, the focus is on assessing trends in annual mean and maximum Hs. The result is compared with other model simulations that account for other sources of uncertainty, and with modern wave reanalyses. It is shown that the trend variability arising from internal climate variability is comparable to the variability caused by other factors, such as climate model uncertainty. This study also assesses the likelihood to mis-estimate trends when using only one ensemble member and therefore one possible realization of the climate system. Using single member failed to detect the statistically significant notable positive trend shown in the ensemble in some areas of the Southern Ocean. The North Atlantic Ocean is found to have large internal climate variability, where different ensemble-members can show trends of the opposite signs for the same area. The minimum ensemble size necessary to effectively reduce the risk of mis-assessing Hs trends is estimated to be 10; but this largely depends on the specific wave statistic and the region of interest, with larger ensembles being required to assess extremes. The results also show that wave reanalyses are not suitable for analyzing Hs trends due to temporal inhomogeneities therein, in agreement with recent studies.

Modelling the integrated strategies of deficit irrigation, nitrogen fertilization, and biochar addition for winter wheat by AquaCrop based on a two-year field study

Biochar application has been proposed as a promising practice for sustainability due to its potential to ensure food security while alleviating the water shortage and fertilizer overuse. Scheduling the proper integrated management of deficit irrigation, nitrogen fertilization, and biochar addition are necessary to facilitate the effective use of biochar in agriculture. The objectives of this study were to test the AquaCrop model for winter wheat under different irrigation, nitrogen, and biochar regimes and to optimize the integrated strategies. For the model calibration and validation, a field experiment of winter wheat with two irrigation levels (full and deficit irrigation) and three fertility levels (200 kg/ha N, 100 kg N + 15 t/ha biochar, 0 kg/ha N) was conducted in the North China Plain during 2017/2018 and 2018/2019 growing seasons. The results showed that AquaCrop performed well in simulating soil water content (0.82 ≤ R 2 , 0.92 ≤ d, RMSE ≤ 2.82%, NRMSE ≤ 17.34%), canopy cover (0.89 ≤ R 2 , 0.95 ≤ d, RMSE ≤ 7.58%, NRMSE ≤ 13.33%), biomass (0.96 ≤ R 2 , 0.96 ≤ d, RMSE ≤ 1.04 t/ha, NRMSE ≤ 16.75%), and grain yield (0.94 ≤ R 2 , 0.97 ≤ d, RMSE ≤ 0.34 t/ha, NRMSE ≤ 5.32%). The effects of biochar on soil moisture, fertility, and wheat production could be adequately simulated by AquaCrop using the hydraulic properties of the biochar-mixed soil layer and its semi-quantitative method for soil fertility stress. The optimal integrated strategies were deficit irrigation with three irrigations of 60 mm at the jointing, anthesis, and grain-filling stage for dry years, two irrigations of 60 mm at the jointing and anthesis stage for normal years, and one irrigation of 60 mm at the anthesis stage for wet years, combined with 100 kg/ha N and 15 t/ha biochar, which could achieve over 80% of the potential yield while substantially increasing irrigation water productivity. Nonetheless, the model tended to under-estimate soil moisture, leaf growth and senescence, and grain yield in the biochar amended treatments, particularly under drought. Further improvements in the modules of soil properties, soil water balance, and canopy cover can be performed to increase the estimation accuracy. Additionally, AquaCrop was only calibrated and validated based on a two-year experiment under the weather conditions of no drought and moderate drought. Therefore, future validation studies using the long-term field data with the larger climate variability are necessary to better identify the impacts of integrated strategies and climate change. Finally, considering its good balance in simplicity, robustness, and accuracy, AquaCrop could be a reliable tool to guide the field management of irrigation, fertilization, and biochar addition. • AquaCrop was tested under various irrigation, nitrogen, biochar regimes. • AquaCrop adequately simulated the effects of biochar on winter wheat. • Integrated strategies of irrigation, nitrogen, biochar were optimized. • Optimal integrated strategies could achieve over 80% of the potential yield.

Energy and physiological tolerance explain multi‐trophic soil diversity in temperate mountains

AbstractAimAlthough soil biodiversity is extremely rich and spatially variable, both in terms of species and trophic groups, we still know little about its main drivers. Here, we contrast four long‐standing hypotheses to explain the spatial variation of soil multi‐trophic diversity: energy, physiological tolerance, habitat heterogeneity and resource heterogeneity.LocationFrench Alps.MethodsWe built on a large‐scale observatory across the French Alps (Orchamp) made of seventeen elevational gradients (~90 plots) ranging from low to very high altitude (280–3,160 m), and encompassing large variations in climate, vegetation and pedological conditions. Biodiversity measurements of 36 soil trophic groups were obtained through environmental DNA metabarcoding. Using a machine learning approach, we assessed (1) the relative importance of predictors linked to different ecological hypotheses in explaining overall multi‐trophic soil biodiversity and (2) the consistency of the response curves across trophic groups.ResultsWe showed that predictors associated with the four hypotheses had a statistically significant influence on soil multi‐trophic diversity, with the strongest support for the energy and physiological tolerance hypotheses. Physiological tolerance explained spatial variation in soil diversity consistently across trophic groups, and was an especially strong predictor for bacteria, protists and microfauna. The effect of energy was more group‐specific, with energy input through soil organic matter strongly affecting groups related to the detritus channel. Habitat and resource heterogeneity had overall weaker and more specific impacts on biodiversity with habitat heterogeneity affecting mostly autotrophs, and resource heterogeneity affecting bacterivores, phytophagous insects, enchytraeids and saprotrophic fungi.Main ConclusionsDespite the variability of responses to the environmental drivers found across soil trophic groups, major commonalities on the ecological processes structuring soil biodiversity emerged. We conclude that among the major ecological hypotheses traditionally applied to aboveground organisms, some are particularly relevant to predict the spatial variation in soil biodiversity across the major soil trophic groups.

Assessing Groundwater Withdrawal Sustainability in the Mexican Portion of the Transboundary Santa Cruz River Aquifer

The impact of climate uncertainties is already evident in the border communities of the United States and Mexico. This semi-arid to arid border region has faced increased vulnerability to water scarcity, propelled by droughts, warming atmosphere, population growth, ecosystem sensitivity, and institutional asymmetries between the two countries. In this study, we assessed the annual water withdrawal, which is essential for maintaining long-term sustainable conditions in the Santa Cruz River Aquifer in Mexico, which is part of the U.S.–Mexico Transboundary Santa Cruz Aquifer. For this assessment, we developed a water balance model that accounts for the water fluxes into and out of the aquifer’s basin. A central component of this model is a hydrologic model that uses precipitation and evapotranspiration demand as input to simulate the streamflow into and out of the basin, natural recharge, soil moisture, and actual evapotranspiration. Based on the precipitation record for the period 1954–2020, we found that the amount of groundwater withdrawal that maintains sustainable conditions is 23.3 MCM/year. However, the record is clearly divided into two periods: a wet period, 1965–1993, in which the cumulative surplus in the basin reached ~380 MCM by 1993, and a dry period, 1994–2020, in which the cumulative surplus had been completely depleted. Looking at a balanced annual groundwater withdrawal for a moving average of 20-year intervals, we found the sustainable groundwater withdrawal to decline from a maximum of 36.4 MCM/year in 1993 to less than 8 MCM/year in 2020. This study underscores the urgency for adjusted water resources management that considers the large inter-annual climate variability in the region.

Robust detection of forced warming in the presence of potentially large climate variability.

Climate warming is unequivocal and exceeds internal climate variability. However, estimates of the magnitude of decadal-scale variability from models and observations are uncertain, limiting determination of the fraction of warming attributable to external forcing. Here, we use statistical learning to extract a fingerprint of climate change that is robust to different model representations and magnitudes of internal variability. We find a best estimate forced warming trend of 0.8°C over the past 40 years, slightly larger than observed. It is extremely likely that at least 85% is attributable to external forcing based on the median variability across climate models. Detection remains robust even when evaluated against models with high variability and if decadal-scale variability were doubled. This work addresses a long-standing limitation in attributing warming to external forcing and opens up opportunities even in the case of large model differences in decadal-scale variability, model structural uncertainty, and limited observational records.

Cyclists' Perception of Maintenance and Operation of Cycling Infrastructure-Results From a Norwegian Survey.

The Norwegian authorities want to limit the extent of car use in city areas to existing levels. Such a limitation would help combat climate change, improve health of citizens, and alleviate congestion. This implies that any further increase in transport needs will have to be met by walking, cycling and use of public transport. Reaching this ambitious goal requires knowledge about cyclists' preferences concerning operation and maintenance (M&O) of roads and foot/cycle paths. Previous research suggests that M&O have great implications for travel mode choice, bicycle route/path choice, safety, security, and comfort. With the need to serve bicyclists of all ages and genders, this study additionally explores which M&O of roads and foot/cycle the different demographic groups perceive positively or negatively. This article reports results from a nationwide survey in the summer of 2019. Two thousand three hundred seventy-six cyclists across Norway (55% male; 29% <40; 17% >60) participated to determine the cyclists' perceptions about year-round M&O of roads and foot/cycle paths. Respondents, rather than being randomly selected, completed an internet-linked survey. The variables included maintenance of foot/cycle paths in terms of salt and snow plowing and operation and maintenance of roads in terms of glass, holes/bumps, and conditions. Our results suggest that female cyclists suffer more from adverse conditions than do males. We also find that males are more likely to cycle during winter, which is an additional indication that adverse conditions affect women and men differently. Surprisingly, older cyclists report to be less affected by poor conditions than younger cyclists. Self-selection to participate in the survey among older cyclists might be an important explanation for this result. Cycling conditions vary greatly between geographical areas, reflecting the large climatic variations across Norway. Most respondents have experienced a cycle accident where conditions contributed, and many sometimes forfeit cycling due to adverse conditions. Implications for future research and practice of M&O are discussed.

Surface temperature variability in climate models with large and small internal climate variability

AbstractBy analyzing large ensemble simulations using the Community Earth System Model (CESM_LE), the Max Planck Institute Earth System Model Grand Ensemble (MPI_GE), and Coupled Model Intercomparison Project phase 5 (CMIP5) climate models, we quantified internal climate variability (ICV) of surface temperature in each model based on the spread of simulated global mean surface temperature from the ensemble mean. Then, we examined the characteristics of simulated surface temperature variability in climate models with large and small ICV in the present climate and in a future climate. Both the CESM_LE and MPI_GE members with large ICVs tended to simulate larger surface temperature variability at low latitudes, including El Niño and Southern Oscillation (ENSO) variability, and larger cooling and warming trends of the global mean surface temperatures than those with small ICVs in the present climate. Similar characteristics were observed in CMIP5 climate models with large and small ICVs in the present climate. This implies that surface temperature variability including extreme climate events should be cautiously examined in climate models with large and small ICVs. On the other hand, the characteristics of surface temperature variability simulated in the CMIP5 climate models with large or small ICVs were similar from the present climate to future climate with magnitude of ICVs. This was in contrast to that simulated in the CESM_LE and MPI_GE, in which the magnitude of ICV changes between the present climate and the future climate. We inferred that these differences between CMIP5 climate models and large ensemble simulations could primarily be attributed to intermodel differences in the CMIP5 climate models, including model physics and parameterizations.