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Abstract Lignin and cellulose are thought to be critical factors that affect the rate of litter decomposition; however, few data are available on their degradation dynamics during litter decomposition in lotic ecosystems, such as forest rivers, where litter can decompose much more rapidly than in terrestrial ecosystems. We studied the degradation of lignin and cellulose in the foliar litter of four dominant riparian species (willow: Salix paraplesia ; azalea: Rhododendron lapponicum ; cypress: Sabina saltuaria ; and larch: Larix mastersiana ) in an alpine forest river. Over an entire year's incubation, litter lignin and cellulose degraded by 14.7–100% and 57.7–100% of their initial masses, respectively, depending on litter species. Strong degradations of lignin and cellulose occurred in the prefreezing period (i.e., the first 41 d) during litter decomposition, and the degradation rate was the highest among all the decomposition periods regardless of litter species. Litter species, decomposition period, and environmental factors such as temperature and nutrient availability showed significant influences on lignin and cellulose degradation rates. Compared with previously reported data regarding the dynamics of lignin and cellulose during litter decomposition in terrestrial ecosystems, our results suggest that lignin and cellulose can be degraded much more rapidly in lotic ecosystems, indicating that the traditionally used two‐phased model for the dynamics of lignin in decomposing litter may not be suitable in lotic ecosystems.

Summary For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.

Summary For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.

Abstract The interdependence between climatic variables should be taken into account when developing climate scenarios. For example, temperature‐precipitation interdependence in the Arctic is strong and impacts on other physical characteristics, such as the extent and duration of snow cover. However, this interdependence is often misrepresented in climate simulations. Here we use two two‐dimensional (2‐D) methods for statistically adjusting climate model simulations to develop plausible local daily temperature ( T mean ) and precipitation ( Pr ) scenarios. The first 2‐D method is based on empirical quantile mapping (2Dqm) and the second on parametric copula models (2Dcopula). Both methods are improved here by forcing the preservation of the modeled long‐term warming trend and by using moving windows to obtain an adjustment specific to each day of the year. These methods were applied to a representative ensemble of 13 global climate model simulations at 26 Canadian Arctic coastal sites and tested using an innovative cross‐validation approach. Intervariable dependence was evaluated using correlation coefficients and empirical copula density plots. Results show that these 2‐D methods, especially 2Dqm, adjust individual distributions of climatic time series as adequately as one common one‐dimensional method (1Dqm) does. Furthermore, although 2Dqm outperforms the other methods in reproducing the observed temperature‐precipitation interdependence over the calibration period, both 2Dqm and 2Dcopula perform similarly over the validation periods. For cases where temperature‐precipitation interdependence is important (e.g., characterizing extreme events and the extent and duration of snow cover), both 2‐D methods are good options for producing plausible local climate scenarios in Canadian Arctic coastal zones. , Key Points We improved two methods for adjusting T mean , Pr , and their dependence in scenarios Methods are tested at Arctic coastal sites where T mean ‐ Pr dependence is crucial Both methods improve the plausibility of the local climate scenarios

Abstract The last deglaciation (20.0–10.0 kyr B.P.) was punctuated by two major cooling events affecting the Northern Hemisphere: the Oldest Dryas (OD; 18.0–14.7 kyr B.P.) and the Younger Dryas (YD; 12.8–11.5 kyr B.P.). Greenland ice core δ 18 O temperature reconstructions suggest that the YD was as cold as the OD, despite a 50 ppmv increase in atmospheric CO 2 , while modeling studies suggest that the YD was approximately 4–5°C warmer than the OD. This discrepancy has been surmised to result from changes in the origin of the water vapor delivered to Greenland; however, this hypothesis has not been hitherto tested. Here we use an atmospheric circulation model with an embedded moisture‐tracing module to investigate atmospheric processes that may have been responsible for the similar δ 18 O values during the OD and YD. Our results show that the summer‐to‐winter precipitation ratio over central Greenland in the OD is twice as high as in the YD experiment, which shifts the δ 18 O signal toward warmer (summer) temperatures (enriched δ 18 O values and it accounts for ~45% of the expected YD‐OD δ 18 O difference). A change in the inversion (cloud) temperature relationship between the two climate states further contributes (~20%) to altering the δ 18 O‐temperature‐relation model. Our experiments also show a 7% decrease of δ 18 O‐depleted precipitation from distant regions (e.g., the Pacific Ocean) in the OD, hence further contributing (15–20%) in masking the actual temperature difference. All together, these changes provide a physical explanation for the ostensible similarity in the ice core δ 18 O temperature reconstructions in Greenland during OD and YD. , Key Points Precipitation seasonality and inversion temperature changes behind YD‐OD δ 18 O enigma Local processes changes accounting up to 65% of the expected YD‐OD δ 18 O difference Moisture transport changes from the Pacific accounting only up to 20% of the expected YD‐OD δ 18 O difference

Abstract Variability in tropical cyclone activity in the eastern Pacific basin has been linked to a wide range of climate factors, yet the dominant factors driving this variability have yet to be identified. Using Poisson regressions and a track clustering method, the authors analyze and compare the climate influence on cyclone activity in this region. The authors show that local sea surface temperature and upper-ocean heat content as well as large-scale conditions in the northern Atlantic are the dominant influence in modulating eastern North Pacific tropical cyclone activity. The results also support previous findings suggesting that the influence of the Atlantic Ocean occurs through changes in dynamical conditions over the eastern Pacific. Using model selection algorithms, the authors then proceed to construct a statistical model of eastern Pacific tropical cyclone activity. The various model selection techniques used agree in selecting one predictor from the Atlantic (northern North Atlantic sea surface temperature) and one predictor from the Pacific (relative sea surface temperature) to represent the best possible model. Finally, we show that this simple model could have predicted the anomalously high level of activity observed in 2014.

Abstract Sources and timing of freshwater forcing relative to hydroclimate shifts recorded in Greenland ice cores at the onset of Younger Dryas, ∼12,800 years ago, remain speculative. Here we show that progressive Fennoscandian Ice Sheet (FIS) melting 13,100–12,880 years ago generates a hydroclimate dipole with drier–colder conditions in Northern Europe and wetter–warmer conditions in Greenland. FIS melting culminates 12,880 years ago synchronously with the start of Greenland Stadial 1 and a large-scale hydroclimate transition lasting ∼180 years. Transient climate model simulations forced with FIS freshwater reproduce the initial hydroclimate dipole through sea-ice feedbacks in the Nordic Seas. The transition is attributed to the export of excess sea ice to the subpolar North Atlantic and a subsequent southward shift of the westerly winds. We suggest that North Atlantic hydroclimate sensitivity to FIS freshwater can explain the pace and sign of shifts recorded in Greenland at the climate transition into the Younger Dryas.

Significance In the model simulations analyzed here, large high-latitude volcanic eruptions have global and long-lasting effects on climate, altering the spatiotemporal characteristic of the El Niño–Southern Oscillation (ENSO) on both short (<1 y) and long timescales and affecting the strength of the Atlantic Meridional Overturning Circulation (AMOC). In the first 8–9 mo following the start of the eruption, El Niño-like anomalies develop over the equatorial Pacific. The large high-latitude eruptions also trigger a strengthening of the AMOC in the first 25 y after the eruption, which is associated with an increase in ENSO variability. This is then followed by a weakening of the AMOC lasting another 30–35 y, associated with decreased ENSO variability. , Large volcanic eruptions can have major impacts on global climate, affecting both atmospheric and ocean circulation through changes in atmospheric chemical composition and optical properties. The residence time of volcanic aerosol from strong eruptions is roughly 2–3 y. Attention has consequently focused on their short-term impacts, whereas the long-term, ocean-mediated response has not been well studied. Most studies have focused on tropical eruptions; high-latitude eruptions have drawn less attention because their impacts are thought to be merely hemispheric rather than global. No study to date has investigated the long-term effects of high-latitude eruptions. Here, we use a climate model to show that large summer high-latitude eruptions in the Northern Hemisphere cause strong hemispheric cooling, which could induce an El Niño-like anomaly, in the equatorial Pacific during the first 8–9 mo after the start of the eruption. The hemispherically asymmetric cooling shifts the Intertropical Convergence Zone southward, triggering a weakening of the trade winds over the western and central equatorial Pacific that favors the development of an El Niño-like anomaly. In the model used here, the specified high-latitude eruption also leads to a strengthening of the Atlantic Meridional Overturning Circulation (AMOC) in the first 25 y after the eruption, followed by a weakening lasting at least 35 y. The long-lived changes in the AMOC strength also alter the variability of the El Niño–Southern Oscillation (ENSO).

Based on a new process-based model, TRIPLEX-GHG, this paper analyzed the spatio-temporal variations of natural wetland CH4 emissions over China under different future climate change scenarios. When natural wetland distributions were fixed, the amount of CH4 emissions from natural wetland ecosystem over China would increase by 32.0%, 55.3% and 90.8% by the end of 21st century under three representative concentration pathways (RCPs) scenarios, RCP2. 6, RCP4.5 and RCP8.5, respectively, compared with the current level. Southern China would have higher CH4 emissions compared to that from central and northern China. Besides, there would be relatively low emission fluxes in western China while relatively high emission fluxes in eastern China. Spatially, the areas with relatively high CH4 emission fluxes would be concentrated in the middle-lower reaches of the Yangtze River, the Northeast and the coasts of the Pearl River. In the future, most natural wetlands would emit more CH4 for RCP4.5 and RCP8.5 than that of 2005. However, under RCP2.6 scenario, the increasing trend would be curbed and CH4 emissions (especially from the Qinghai-Tibet Plateau) begin to decrease in the late 21st century.

Abstract. Reconstructions of Quaternary climate are often based on the isotopic content of paleo-precipitation preserved in proxy records. While many paleo-precipitation isotope records are available, few studies have synthesized these dispersed records to explore spatial patterns of late-glacial precipitation δ18O. Here we present a synthesis of 86 globally distributed groundwater (n = 59), cave calcite (n = 15) and ice core (n = 12) isotope records spanning the late-glacial (defined as ~ 50 000 to ~ 20 000 years ago) to the late-Holocene (within the past ~ 5000 years). We show that precipitation δ18O changes from the late-glacial to the late-Holocene range from −7.1 ‰ (δ18Olate-Holocene > δ18Olate-glacial) to +1.7 ‰ (δ18Olate-glacial > δ18Olate-Holocene), with the majority (77 %) of records having lower late-glacial δ18O than late-Holocene δ18O values. High-magnitude, negative precipitation δ18O shifts are common at high latitudes, high altitudes and continental interiors (δ18Olate-Holocene > δ18Olate-glacial by more than 3 ‰). Conversely, low-magnitude, positive precipitation δ18O shifts are concentrated along tropical and subtropical coasts (δ18Olate-glacial > δ18Olate-Holocene by less than 2 ‰). Broad, global patterns of late-glacial to late-Holocene precipitation δ18O shifts suggest that stronger-than-modern isotopic distillation of air masses prevailed during the late-glacial, likely impacted by larger global temperature differences between the tropics and the poles. Further, to test how well general circulation models reproduce global precipitation δ18O shifts, we compiled simulated precipitation δ18O shifts from five isotope-enabled general circulation models simulated under recent and last glacial maximum climate states. Climate simulations generally show better inter-model and model-measurement agreement in temperate regions than in the tropics, highlighting a need for further research to better understand how inter-model spread in convective rainout, seawater δ18O and glacial topography parameterizations impact simulated precipitation δ18O. Future research on paleo-precipitation δ18O records can use the global maps of measured and simulated late-glacial precipitation isotope compositions to target and prioritize field sites.

Objectives: We propose a novel approach to examine vulnerability in the relationship between heat and years of life lost and apply to neighborhood social disparities in Montreal and Paris. Methods: We used historical data from the summers of 1990 through 2007 for Montreal and from 2004 through 2009 for Paris to estimate daily years of life lost social disparities (DYLLD), summarizing social inequalities across groups. We used Generalized Linear Models to separately estimate relative risks (RR) for DYLLD in association with daily mean temperatures in both cities. We used 30 climate scenarios of daily mean temperature to estimate future temperature distributions (2021–2050). We performed random effect meta-analyses to assess the impact of climate change by climate scenario for each city and compared the impact of climate change for the two cities using a meta-regression analysis. Results: We show that an increase in ambient temperature leads to an increase in social disparities in daily years of life lost. The impact of climate change on DYLLD attributable to temperature was of 2.06 (95% CI: 1.90, 2.25) in Montreal and 1.77 (95% CI: 1.61, 1.94) in Paris. The city explained a difference of 0.31 (95% CI: 0.14, 0.49) on the impact of climate change. Conclusion: We propose a new analytical approach for estimating vulnerability in the relationship between heat and health. Our results suggest that in Paris and Montreal, health disparities related to heat impacts exist today and will increase in the future.