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Abstract. We use a high-resolution regional climate model to investigate the changes in Atlantic tropical cyclone (TC) activity during the period of the mid-Holocene (MH: 6000 years BP) with a larger amplitude of the seasonal cycle relative to today. This period was characterized by increased boreal summer insolation over the Northern Hemisphere, a vegetated Sahara and reduced airborne dust concentrations. A set of sensitivity experiments was conducted in which solar insolation, vegetation and dust concentrations were changed in turn to disentangle their impacts on TC activity in the Atlantic Ocean. Results show that the greening of the Sahara and reduced dust loadings (MHGS+RD) lead to a larger increase in the number of Atlantic TCs (27 %) relative to the pre-industrial (PI) climate than the orbital forcing alone (MHPMIP; 9 %). The TC seasonality is also highly modified in the MH climate, showing a decrease in TC activity during the beginning of the hurricane season (June to August), with a shift of its maximum towards October and November in the MHGS+RD experiment relative to PI. MH experiments simulate stronger hurricanes compared to PI, similar to future projections. Moreover, they suggest longer-lasting cyclones relative to PI. Our results also show that changes in the African easterly waves are not relevant in altering the frequency and intensity of TCs, but they may shift the location of their genesis. This work highlights the importance of considering vegetation and dust changes over the Sahara region when investigating TC activity under a different climate state.

Abstract. The continental divide along the spine of the Canadian Rockies in southwestern Canada is a critical headwater region for hydrological drainages to the Pacific, Arctic, and Atlantic oceans. Major flooding events are typically attributed to heavy precipitation on its eastern side due to upslope (easterly) flows. Precipitation can also occur on the western side of the divide when moisture originating from the Pacific Ocean encounters the west-facing slopes of the Canadian Rockies. Often, storms propagating across the divide result in significant precipitation on both sides. Meteorological data over this critical region are sparse, with few stations located at high elevations. Given the importance of all these types of events, the Storms and Precipitation Across the continental Divide Experiment (SPADE) was initiated to enhance our knowledge of the atmospheric processes leading to storms and precipitation on either side of the continental divide. This was accomplished by installing specialized meteorological instrumentation on both sides of the continental divide and carrying out manual observations during an intensive field campaign from 24 April–26 June 2019. On the eastern side, there were two field sites: (i) at Fortress Mountain Powerline (2076 m a.s.l.) and (ii) at Fortress Junction Service, located in a high-elevation valley (1580 m a.s.l.). On the western side, Nipika Mountain Resort, also located in a valley (1087 m a.s.l.), was chosen as a field site. Various meteorological instruments were deployed including two Doppler light detection and ranging instruments (lidars), three vertically pointing micro rain radars, and three optical disdrometers. The three main sites were nearly identically instrumented, and observers were on site at Fortress Mountain Powerline and Nipika Mountain Resort during precipitation events to take manual observations of precipitation type and microphotographs of solid particles. The objective of the field campaign was to gather high-temporal-frequency meteorological data and to compare the different conditions on either side of the divide to study the precipitation processes that can lead to catastrophic flooding in the region. Details on field sites, instrumentation used, and collection methods are discussed. Data from the study are publicly accessible from the Federated Research Data Repository at https://doi.org/10.20383/101.0221 (Thériault et al., 2020). This dataset will be used to study atmospheric conditions associated with precipitation events documented simultaneously on either side of a continental divide. This paper also provides a sample of the data gathered during a precipitation event.

Abstract Forest soils play an important role in controlling global warming by reducing atmospheric methane (CH 4 ) concentrations. However, little attention has been paid to how nitrogen (N) deposition may alter microorganism communities that are related to the CH 4 cycle or CH 4 oxidation in subtropical forest soils. We investigated the effects of N addition (0, 30, 60, or 90 kg N ha −1  yr −1 ) on soil CH 4 flux and methanotroph and methanogen abundance, diversity, and community structure in a Moso bamboo ( Phyllostachys edulis ) forest in subtropical China. N addition significantly increased methanogen abundance but reduced both methanotroph and methanogen diversity. Methanotroph and methanogen community structures under the N deposition treatments were significantly different from those of the control. In N deposition treatments, the relative abundance of Methanoculleus was significantly lower than that in the control. Soil pH was the key factor regulating the changes in methanotroph and methanogen diversity and community structure. The CH 4 emission rate increased with N addition and was negatively correlated with both methanotroph and methanogen diversity but positively correlated with methanogen abundance. Overall, our results suggested that N deposition can suppress CH 4 uptake by altering methanotroph and methanogen abundance, diversity, and community structure in subtropical Moso bamboo forest soils.

Abstract Ecosystem-level effects of increasing atmospheric nitrogen (N) deposition on the phosphorus (P) cycle and P use strategy are poorly understood. Here, we conducted a seven year N-addition experiment to comprehensively evaluate the effects of N deposition on P limitation, cycling, and use strategy in a subtropical Moso bamboo forest. N addition significantly increased foliar litterfall by 4.7%–21.7% and subsequent P return to the soil by 49.0%–70.1%. It also increased soil acidity, acid phosphatase activity, and soil microbial biomass P, which substantially contributed to a significantly increased soil P availability and largely alleviated the P limitation. This resulted in a significant decrease in the foliar P-resorption efficiency and the abundance and colonization of arbuscular mycorrhizal fungi. Our results indicate that N deposition can reduce plant internal cycling while enhancing ecosystem-scale cycling of P in Moso bamboo forests. This suggests a shift in P use from a ‘conservative consumption’ strategy to a ‘resource spending’ strategy. Our findings shed new light on N deposition effects on P cycle processes and P use strategy at the ecosystem scale under increasing atmospheric N deposition.

Abstract Vegetation restoration has been proposed as an effective measure for rehabilitating degraded land and slowing desertification in arid regions. However, the spatial variation in soil quality and plant diversity following vegetation restoration remains unclear. This study was designed to explore soil nutrient dynamics and how soil nutrients affect plant diversity and spatial heterogeneity after shrub restoration. We assessed the effect of Haloxylon ammodendron (C.A.Mey.) Bunge (which has been planted over 30 years) on the soil nutrients and plant diversity in a desert–oasis ecotone in Minqin County, Gansu, China, using geostatistics, beta diversity and rarefaction analyses, and Hill number extrapolation. Soil nutrients, including soil organic matter, total nitrogen, and alkali nitrogen, increased significantly after H. ammodendron planting. Species richness gradually increased from 1–5 years to 10–20 years after H. ammodendron was planted but then decreased at 20–30 years. The largest differences in plant composition were observed at 15 and 20 years. Plant diversity increased in the whole 30 years after shrub planting, increasing in the first 25 years and then decreasing at 26–30 year stage. The maximum coefficient of determination for the spatial heterogeneity model fit was 0.84 (25 years). The spatial heterogeneity in vegetation decreased with increasing soil available K content at 1–10 years. Our results suggest that planting shrubs can improve soil conditions and plant species diversity in desert–oasis ecotones and soil nutrients have a strong influence on plant diversity patterns and spatial heterogeneity following vegetation restoration.

Abstract. The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Abstract. The modeling of paleoclimate, using physically based tools, is increasingly seen as a strong out-of-sample test of the models that are used for the projection of future climate changes. New to the Coupled Model Intercomparison Project (CMIP6) is the Tier 1 Last Interglacial experiment for 127 000 years ago (lig127k), designed to address the climate responses to stronger orbital forcing than the midHolocene experiment, using the same state-of-the-art models as for the future and following a common experimental protocol. Here we present a first analysis of a multi-model ensemble of 17 climate models, all of which have completed the CMIP6 DECK (Diagnostic, Evaluation and Characterization of Klima) experiments. The equilibrium climate sensitivity (ECS) of these models varies from 1.8 to 5.6 ∘C. The seasonal character of the insolation anomalies results in strong summer warming over the Northern Hemisphere continents in the lig127k ensemble as compared to the CMIP6 piControl and much-reduced minimum sea ice in the Arctic. The multi-model results indicate enhanced summer monsoonal precipitation in the Northern Hemisphere and reductions in the Southern Hemisphere. These responses are greater in the lig127k than the CMIP6 midHolocene simulations as expected from the larger insolation anomalies at 127 than 6 ka. New synthesis for surface temperature and precipitation, targeted for 127 ka, have been developed for comparison to the multi-model ensemble. The lig127k model ensemble and data reconstructions are in good agreement for summer temperature anomalies over Canada, Scandinavia, and the North Atlantic and for precipitation over the Northern Hemisphere continents. The model–data comparisons and mismatches point to further study of the sensitivity of the simulations to uncertainties in the boundary conditions and of the uncertainties and sparse coverage in current proxy reconstructions. The CMIP6–Paleoclimate Modeling Intercomparison Project (PMIP4) lig127k simulations, in combination with the proxy record, improve our confidence in future projections of monsoons, surface temperature, and Arctic sea ice, thus providing a key target for model evaluation and optimization.

We address here the specific timing and amplitude of sea‐surface conditions and productivity changes off SW Greenland, northern Labrador Sea, in response to the high deglacial meltwater rates, the Early Holocene maximum insolation and Neoglacial cooling. Dinocyst assemblages from sediment cores collected off Nuuk were used to set up quantitative records of sea ice cover, seasonal sea‐surface temperature (SST), salinity (SSS), and primary productivity, with a centennial to millennial scale resolution. Until ~10 ka BP, ice‐proximal conditions are suggested by the quasi‐exclusive dominance of heterotrophic taxa and low dinocyst concentrations. At about 10 ka BP, an increase in species diversity and abundance of phototrophic taxa marks the onset of interglacial conditions at a regional scale, with summer SST reaching up to 10 °C between 8 and 5 ka BP, thus in phase with the Holocene Thermal Maximum as recorded in the southern Greenlandic areas/northern Labrador Sea. During this interval, low SSS but high productivity prevailed in response to high meltwater discharge and nutrient inputs from the Greenland Ice Sheet. After ~5 ka BP, a decrease in phototrophic taxa marks a two‐step cooling of surface waters. The first started at ~5 ka BP, and the second at ~3 ka BP, with a shift toward colder conditions and higher SSS suggesting reduced meltwater discharge during the Neoglacial. This second step coincides with the disappearance of the Saqqaq culture. The gap in human occupation in west Greenland, between the Dorset and the Norse settlements from 2000 to 1000 years BP, might be linked to high amplitude and high frequency variability of ocean and climate conditions.

Analyses of marine and terrestrial palynomorphs of Ocean Drilling Program (ODP) Site 645 in Baffin Bay led us to define a new biostratigraphical scheme covering the late Miocene to Pleistocene based on dinocyst and acritarch assemblages. Four biozones were defined. The first one, from 438.6 m below sea floor (mbsf) to 388 mbsf, can be assigned a late Miocene to early Pliocene age (>4.5 Ma), based on the common occurrence of Cristadinium diminutivum and Selenopemphix brevispinosa. Biozone 2, spanning from an erosional unconformity to a recovery hiatus, is marked by the highest occurrences (HOs) of Veriplicidium franklinii and Cristadinium diminutivum, which suggest an early Pliocene age >3.6 Ma (∼4.5 to ∼3.6 Ma). Biozone 3, above the recovery hiatus and up to 220.94 mbsf, corresponds to a late Pliocene or early Pleistocene age based on occurrences of Bitectatodinium readwaldii, Cymatiosphaera? icenorum, and Lavradosphaera canalis. Finally, between 266.4 and 120.56 mbsf, Biozone 4, marked by the HO of Filisphaera filifera, Filisphaera microornata, and Habibacysta tectata, has an early Pleistocene age (>1.4 Ma). Our biostratigraphy implies that horizon b1 of the Baffin Bay seismic stratigraphy corresponds to the recovery hiatus at ODP Site 645, which suggests a very thick Pliocene sequence along the Baffin Island slope. Dinocyst assemblages and terrestrial palynomorphs in our records indicate that the late Miocene and (or) early Pliocene were characterized by relatively warm coastal surface waters and boreal forest or forested tundra vegetation over adjacent lands. In contrast, the early Pleistocene dinocyst assemblages above the recovery hiatus indicate cold surface waters, while pollen data suggest reduced vegetation cover on adjacent lands.

Abstract Increasing forest soil organic carbon (SOC) storage is important for reducing carbon dioxide (CO 2 ) emissions from terrestrial ecosystems and mitigating global climate change. Although the effects of altitude, temperature and rainfall on organic carbon have been studied extensively, it is difficult to increase SOC storage by changing these factors in actual forest management. This study determined the SOC, soil physical and chemical properties, nutrient elements, heavy metal elements, soil minerals and microbial biomass in the 0–140‐cm soil layer of the monsoon broad‐leaved forest in the acid red soil region of southwestern China by stratification. We tried to identify the soil factors affecting the SOC storage of the forest in the acid red soil region and determine the weights of the factors affecting the SOC, with the aim of improving the SOC retention capacity in forest management by changing the main soil factors affecting SOC storage. The results showed that the soil factors affecting the forest SOC storage in this area are total nitrogen (N, 22.7%) > soil water content (19.9%) > active iron (including poorly crystalline iron, Fe o , 15.5%) > pH (9.5%) > phosphorus (P, 9.4%) > aluminium (Al, 8.9%) > silicon (Si, 7.1%) > sulphur (S, 6.8%). Of these factors, N, the water content, Fe o , and P are practical factors for forest management, whereas the pH, Al, Si and S are not. SOC was significantly positively correlated with the soil N concentration, water content, active iron content and P concentration ( p  < .05). In acidic red soil areas, with active iron as the highlight, N, soil water content, phosphorus and active iron jointly regulate the forest SOC storage capacity. Consequently, in actual forest management, any measures to promote soil N and water content and to activate inactive iron can enhance the storage of SOC, as appropriate input of N and P fertiliser and irrigation in dry years and the dry season. Highlights The soil environmental factors affecting SOC storage in forest soil are quantified Activation of inactive iron helps SOC storage in forest soil Irrigation and N and P input are effective for helping SOC storage in forest soil N, WC, P and Fe o jointly regulate SOC in tropical acid red soil forest

Abstract Lightning climate change projections show large uncertainties caused by limited empirical knowledge and strong assumptions inherent to coarse-grid climate modeling. This study addresses the latter issue by implementing and applying the lightning potential index parameterization (LPI) into a fine-grid convection-permitting regional climate model (CPM). This setup takes advantage of the explicit representation of deep convection in CPMs and allows for process-oriented LPI inputs such as vertical velocity within convective cells and coexistence of microphysical hydrometeor types, which are known to contribute to charge separation mechanisms. The LPI output is compared to output from a simpler flash rate parameterization, namely the CAPE $$\times$$ × PREC parameterization, applied in a non-CPM on a coarser grid. The LPI’s implementation into the regional climate model COSMO-CLM successfully reproduces the observed lightning climatology, including its latitudinal gradient, its daily and hourly probability distributions, and its diurnal and annual cycles. Besides, the simulated temperature dependence of lightning reflects the observed dependency. The LPI outperforms the CAPE $$\times$$ × PREC parameterization in all applied diagnostics. Based on this satisfactory evaluation, we used the LPI to a climate change projection under the RCP8.5 scenario. For the domain under investigation centered over Germany, the LPI projects a decrease of $$4.8\%$$ 4.8 % in flash rate by the end of the century, in opposition to a projected increase of $$17.4\%$$ 17.4 % as projected using the CAPE $$\times$$ × PREC parameterization. The future decrease of LPI occurs mostly during the summer afternoons and is related to (i) a change in convection occurrence and (ii) changes in the microphysical mixing. The two parameterizations differ because of different convection occurrences in the CPM and non-CPM and because of changes in the microphysical mixing, which is only represented in the LPI lightning parameterization.

Abstract Large scale flood risk analyses are fundamental to many applications requiring national or international overviews of flood risk. While large‐scale climate patterns such as teleconnections and climate change become important at this scale, it remains a challenge to represent the local hydrological cycle over various watersheds in a manner that is physically consistent with climate. As a result, global models tend to suffer from a lack of available scenarios and flexibility that are key for planners, relief organizations, regulators, and the financial services industry to analyze the socioeconomic, demographic, and climatic factors affecting exposure. Here we introduce a data‐driven, global, fast, flexible, and climate‐consistent flood risk modeling framework for applications that do not necessarily require high‐resolution flood mapping. We use statistical and machine learning methods to examine the relationship between historical flood occurrence and impact from the Dartmouth Flood Observatory (1985–2017), and climatic, watershed, and socioeconomic factors for 4,734 HydroSHEDS watersheds globally. Using bias‐corrected output from the NCAR CESM Large Ensemble (1980–2020), and the fitted statistical relationships, we simulate 1 million years of events worldwide along with the population displaced in each event. We discuss potential applications of the model and present global flood hazard and risk maps. The main value of this global flood model lies in its ability to quickly simulate realistic flood events at a resolution that is useful for large‐scale socioeconomic and financial planning, yet we expect it to be useful to climate and natural hazard scientists who are interested in socioeconomic impacts of climate. , Plain Language Summary Flood is among the deadliest and most damaging natural disasters. To protect against large scale flood risk, stakeholders need to understand how floods can occur and their potential impacts. Stakeholders rely on global flood models to provide them with plausible flood scenarios around the world. For a flood model to operate at the global scale, climate effects must be represented in addition to hydrological ones to demonstrate how rivers can overflow throughout the world each year. Global flood models often lack the flexibility and variety of scenarios required by many stakeholders because they are computationally demanding. Designed for applications where detailed local flood impacts are not required, we introduce a rapid and flexible global flood model that can generate hundreds of thousands of scenarios everywhere in the world in a matter of minutes. The model is based on a historical flood database from 1985 to 2017 that is represented using an algorithm that learns from the data. With this model, the output from a global climate model is used to simulate a large sample of floods for risk analyses that are coherent with global climate. Maps of the annual average number of floods and number of displaced people illustrate the models results. , Key Points We present a global flood model built using machine learning methods fitted with historical flood occurrences and impacts Forced with a climate model, the global flood model is fast, flexible and consistent with global climate We provide global flood hazard (occurrence) and risk (population displaced) maps over 4,734 watersheds

Abstract The structure and “metabolism” (movement and conversion of goods and energy) of urban areas has caused cities to be identified as “super‐organisms”, placed between ecosystems and the biosphere, in the hierarchy of living systems. Yet most such analogies are weak, and render the super‐organism model ineffective for sustainable development of cities. Via a cluster analysis of 15 shared traits of the hierarchical living system, we found that industrialized cities are more similar to eukaryotic cells than to multicellular organisms; enclosed systems, such as factories and greenhouses, paralleling organelles in eukaryotic cells. We further developed a “super‐cell” industrialized city model: a “eukarcity” with citynucleus (urban area) as a regulating centre, and organaras (enclosed systems, which provide the majority of goods and services) as the functional components, and cityplasm (natural ecosystems and farmlands) as the matrix. This model may improve the vitality and sustainability of cities through planning and management.

Abstract A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors. , Plain Language Summary The simulations of complex physical processes always entail various sources of errors. These errors can be of different sign and can consequently cancel each other out when using traditional performance metrics such as the bias error metric. We present a formal framework that allows us to approximate precipitation according to three terms that describe different aspects of the rainfall field including large‐scale environmental conditions and the frequency and intensity of rainfall. We apply the methodology to a large ensemble of high‐resolution simulations representing the precipitation associated with strong cyclones in eastern Australia. We show that simulations produce precipitation too often, with an intensity that is too weak leading to strong compensation. We further define new error metrics that explicitly quantify the degree of error compensation when simulating precipitation. We show that convection‐permitting simulations consistently improve the performance compared to coarser resolution simulations using parameterized convection and that these improvements are only revealed when using the new error metrics but are not apparent in traditional metrics (e.g., bias). , Key Points Multiple high‐resolution simulations produce precipitation too often with underestimated intensity leading to strong error compensation Errors in precipitation are quantified using novel metrics that prevent error compensation showing value compared with traditional metrics Convection permitting simulations outperform the representation of precipitation compared to simulations using parameterized convection