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Understanding how vegetation resilience responds to climate change is crucial for maintaining ecosystem functions. This study focuses on forest and grassland ecosystems and uses theoretical recovery rate as a measure to assess climate impacts on their resilience over China. Our findings reveal that vegetation resilience varies across aridity‐dependent climate zones, with each zone showing different resilience–aridity relationships. Particularly, semi‐arid zones exhibit the lowest vegetation resilience, where the forest resilience declines as inter‐annual temperature and precipitation variability increases. In zones with sufficient water, the forest resilience remains stable. Grassland resilience decreases with increasing precipitation variability, but is insensitive to inter‐annual temperature variability. Future projections highlight the potential threat of climate change to regions encompassing more than 20% of vegetated areas, particularly in the forest‐grassland ecotones of North China. These findings enhance our understanding of climate‐ecosystem interactions and support the anticipation and management of ecosystem risks under climate change.

Aim Niche‐based species distribution models (SDMs) have become a ubiquitous tool in ecology and biogeography. These models relate species occurrences with the environmental conditions found at these sites. Climatic variables are the most commonly used environmental data and are usually included in SDMs as averages of a reference period (30–50 years). In this study, we analyse the impact of including inter‐annual climatic variability on the estimation of species niches and predicted distributions when assessing plant demographic response to extreme climatic episodes. Location Mediterranean basin, SE Iberian Peninsula. Methods We first characterized species niches with inter‐annual and average climate in the same environmental space. We then compare the respective capacities of climatic suitability obtained from averaged climate‐based and from inter‐annual variability‐based niches to explain population demographic responses to extreme drought. Furthermore, we assessed the relative increase in niche size when including climatic variability for a set of Mediterranean species exhibiting a wide range of distribution areas. Results We found that climatic suitability obtained from inter‐annual variability‐based niches showed higher explanatory capacity than average climate‐based suitability, especially for populations living in climatically marginal conditions, although both niches quantifications significantly explained species demographic responses. In addition, species with restricted distribution ranges increased relatively more their niche space when considering climatic variability, probably because in widely distributed species spatial variability compensates for temporal variability. Main conclusions The common use of climatic averages when characterizing species niches could lead to underestimations of species distribution and misunderstanding of demographic behaviour, with implications for conservation plans derived from SDMs, for example, overestimations of species extinction risk under climate change, or underestimations of alien species invasion’ risk. We highlight that including climatic variability in niche modelling can be particularly important when dealing with species with restricted distribution and populations at the margin of their species niche.

Precipitation variability in space and time has been a focus of research over the past decades. The largest body of literature was essentially focused on long-term changes in average climates and in climate extremes. Analyses of the changes in the inter-annual climate variability (the year-to-year variability), which represent an index of climatic risk, received instead very less attention, but it represents an important issue in order to quantitatively measure the socioeconomic impact of climate change impact over water resources. In order to depict a general characterization of the long-term climate variability for the Campania region, located in Southern Italy within the Mediterranean basin, an analysis of the precipitation coefficient of variation, assumed as an index of inter-annual climate variability, was performed over the period 1918–2015 and compared with the annual precipitation regime and the intra-annual precipitation variability of the same region. The Mann–Kendall and the modified Mann–Kendall tests were applied to detect the sign and significance of the temporal changes and Sen’s test was applied to quantify the temporal changes in inter-annual variability. The results illustrated a generalized condition (73% of total stations) of statistically significant increase of inter-annual variability distributed almost over the whole analyzed area, even though the detected change appeared rather moderate in magnitude. The relationship between annual precipitation, intra-annual precipitation variability, and inter-annual precipitation variability was not clearly identified for the studied region, likely because of the characteristics of climatic homogeneity for the area under investigation. However, the comparative analyzes clearly showed how, if the variations in the annual precipitation regime and in the intra-annual precipitation variability are poorly significant (respectively for 9% and 11% of total station), changes in inter-annual precipitation variability are strongly marked over the studied region.

This study investigates the impact of the lower‐thermospheric winter‐to‐summer circulation on the thermosphere's thermal structure and meridional circulation. Using NCAR TIE‐GCM, we compare simulations with and without the lower‐thermospheric circulation, finding that its inclusion enhances summer‐to‐winter thermospheric circulation by 40% in the summer hemisphere but decelerates it in the winter thermosphere. Meanwhile, vertical wind exhibits stronger upward motion poleward of ±30° $\\pm 30{}^{\\circ}$ latitude above 10−6 ${10}^{-6}$ hPa (∼ ${\\sim} $174 km) when lower‐thermospheric circulation is incorporated. This dynamic coupling functions as an atmospheric “gear mechanism,” accelerating momentum and energy transfer to higher altitudes. Including lower‐thermospheric circulation improves agreement between the nudged run and NRLMSIS 2.1 in intra‐annual variability (IAV) of mass density. This suggests lower‐thermospheric circulation is a key factor in modulating IAV in the coupled thermosphere‐ionosphere system. This study reveals a new coupling mechanism between the lower atmosphere, thermosphere, and ionosphere, with significant implications for understanding upper‐atmospheric dynamics and improving space weather models.

Projected changes in the extra‐tropical wintertime storm tracks are investigated using the multi‐model ensembles from both the third and fifth phases of the World Climate Research Programme's Coupled Model Intercomparison Project (CMIP3 and CMIP5). The aim is to characterize the magnitude of the storm track responses relative to their present‐day year‐to‐year variability. For the experiments considered, the ‘middle‐of‐the‐road’ scenarios in each CMIP, there are regions of the Northern Hemisphere where the responses of up to 40% of the models exceed half of the inter‐annual variability, and for the Southern Hemisphere there are regions where up to 60% of the model responses exceed half of the inter‐annual variability. Key Points Storm track responses can be the order of inter‐annual variations There are significant differences between the CMIP3 and CMIP5 storm responses Variability should be used as a measure of the size of climate change responses

1. Trees commonly reproduce via masting cycles, which involves synchronized inter-annual variability in fruit crop size. A few individuals in a population will commonly produce much more fruit than others. If these trees produce fruit more frequently, as indicated by a lower inter-annual variability in fruit production, they may dominate fruit production over time. 2. By measuring fruit production of 1635 individuals of 10 temperate tree species across 4 years in northern lower Michigan, we estimated the inter-annual variability and synchrony in each species. We compared fruit production estimates with measurements of tree size, soil nutrient availability and neighbourhood crowding to investigate the source of inter-individual variation in number of fruit produced. 3. We found that trees' fruit production increased with tree size. The trees that accounted for the largest proportion of total fruit production had lower inter-annual variability and higher synchrony in fruit production. These 'super-producer' trees tended to have high nutrient availability and few neighbouring trees, but there were no effects of nutrient availability or neighbourhood crowding on fruit production in the population as a whole. 4. Synthesis. Masting is a population-level phenomenon, and is typically studied at this level. However, when we apply individual tree observations of fruit production to this phenomenon, it reveals super-producers which produce fruit more consistently than the rest of the population. By reducing inter-annual variability in fruit production, but increasing synchrony and making large numbers of fruit, super-producers may be able to reap the benefits of masting while governing population fruit production over time.

This work addresses the relationship between major dynamical forcings and variability in NO2 column measurements. The dominating impact in Northern Southeast Asia is due to El Niño-Southern Oscillation (ENSO); in Indonesia, Northern Australia and South America is due to Indian Ocean Dipole (IOD); and in Southern China Land and Sea, Populated Northern China, Siberia, Northern and Arctic Eurasia, Central and Southern Africa, and Western US and Canada is due to North Atlantic Oscillation (NAO). That NO2 pollution in Indonesia is modulated by IOD contradicts previous work claiming that the emissions in Indonesia are a function of El Niño impacting upon Aerosol Optical Depth and Fire Radiative Power. Simultaneous impacts of concurrent and lagged forcings are derived using multi-linear regression, demonstrating ENSO impacts future NO2 variability, enhancing NO2 emissions 7–88 weeks in the future, while IOD and NAO in some cases increase the emissions from or the duration of the future burning season as measured by NO2. This impact will also extend to co-emitted aerosols and heat, which may impact the climate. In all cases, lagged forcings exhibit more impact than concurrent forcings, hinting at non-linearity coupling with soil moisture, water table, and other dynamical effects. The regression model formed demonstrates that dynamical forcings are responsible for over 45% of the NO2 emissions variability in most non-urban areas and over 30% in urban China and sub-arctic regions. These results demonstrate the significance of climate forcing on short-lived air pollutants.

Low-flow hydrological features are crucial for efficient development and integrated water resources management. Among others, the BaseFlow Index ‘BFI’ is one of the most important low-flow indices. Many studies have demonstrated that it is related to several topographic parameters, climate, vegetation and soil types and to catchment geology. With the aim to enhance the knowledge about the climate and catchment properties’ relative control on the ‘BFI’, an approach consisting of an empirical analysis, applied to a large area located in Southern Italy, characterized by a typical Mediterranean environment, is followed by a simulation experiment, considering climate settings, at the pan-European scale, typical of temperate to dry climate regimes. Main findings have revealed that (i) the correlation structure between the ‘BFI’ and the precipitation volume, at the annual scale, is affected by both climate variability and catchment properties; (ii) the ‘BFI’ variability is strongly conditioned by climate intra- and inter-annual variability; (iii) the major role is, however, assigned to the geological catchment features, with poorly and well-drained catchments behaving differently in response to similar climate forcing variability.

While the impacts of climate change on global food security have been studied extensively, the capability of emerging tools that couple land surface processes and crop growth in reproducing inter‐annual yield variability at regional scale remains to be tested rigorously. In this study, we analyzed the effects of weather variations between years (1999–2019) on regional crop productivity for two agriculturally managed regions with contrasting climate and cropping conditions: the German state of North Rhine‐Westphalia (DE‐NRW) and the Australian state of Victoria (AUS‐VIC), using the latest version of the Community Land Model (CLM5) and the WFDE5 (WATCH Forcing Data methodology applied to ECMWF reanalysis version 5) reanalysis. Overall, the simulation results were able to reproduce the total annual crop yields of certain crops, while also capturing the differences in total yield magnitudes between the domains. However, the simulations showed limitations in correctly capturing inter‐annual differences of crop yield compared to official yield records, which resulted in relatively low correlation coefficients between 0.07 and 0.39 in AUS‐VIC and between 0.11 and 0.42 in DE‐NRW. The mean absolute deviation of simulated winter wheat yields was up to 4.6 times lower compared to state‐wide records from 1999 to 2019. Our results suggest the following limitations of CLM5: (a) limitations in simulating yield responses from plant hydraulic stress; (b) errors in simulating soil moisture contents compared to satellite‐derived data; and (c) errors in the representation of cropland in general, for example, crop parameterizations and human influences. Plain Language Summary This study evaluates how year‐to‐year weather variations impact crop yield predictions for two regions, North Rhine‐Westphalia in Germany and Victoria in Australia changes. We use the community land model (CLM5) land surface model in combination with reanalysis weather data to investigate the model performance with respect to the representation of crop phenology, plant water stress, and soil moisture. Our results showcase the model's ability to predict total annual crop yield magnitudes for both regions, while also capturing the differences between the respective simulation domains. However, year‐to‐year changes in crop yield were lower in simulation results compared to official records, which indicated a lack of model sensitivity toward drought stress and general limitations in the representation of agricultural land. This research systematically assesses CLM5 model performance over arable land and provides useful insights into limitations of CLM5 that can help guide future empirical and technical model improvements. Key Points Land surface models (LSMs) with integrated crop models can be used to quantify the impact of climate change on agro‐ecosystems The potential value of LSMs for agricultural purposes depends on their ability to adequately simulate inter‐annual variability of yield The representation of plant hydraulics and the soil moisture regime play key role in accurately simulating agro‐ecosystems

Climate change is expected to alter not only year‐to‐year variation in climate but also aspects of within‐year variation, such as the length of the intervals between rainfall events and the duration of heat waves. Yet we still have a poor understanding of how intra‐annual climate variability and individual weather events alter key vital rates (e.g. individual growth, reproduction and survival) that in part determine population dynamics. Traditionally, ecologists have accounted for this variability across long time periods by attempting to correlate annual vital rates with measures of within‐year variability (e.g. the coefficient of variation) to match the scale at which demographic data are collected. An alternative to this aggregate approach is to use within‐season yet still relatively infrequent censuses in a probabilistic framework to determine the most likely way that vital rates respond to shorter‐term variation in the environment, and how these short‐term changes cumulatively lead to the observed yearly vital rates. Here, I present an approach for inferring daily responses of vital rates to short‐term weather variability, and apply it to understand how five species of summer annual plants in the Chihuahuan Desert will respond to climate change. Vital rate models reveal that species differ in their responses to above‐ and below‐average conditions, but generally fall into two life histories: (i) species that have fast growth on favourable days, but also experience higher mortality on less favourable days, and (ii) species that have slower growth in the same conditions but lower mortality. Results show that the expected rainfall changes in the Chihuahuan Desert (more late, cool season rainfall and less frequent, more intense rainfall events) could reduce growth and increase mortality of all summer annual plants. Synthesis. My study shows that vital rates can change in response to short‐term variability even when the total amount of rainfall and average temperature, common covariates in demographic models, remain constant. Accounting for changes in short‐term environmental variation in climate change predictions will likely be important in systems with considerable environmental variation between censuses. The approach I present here can be widely applied to understand how short‐term variability and individual weather events will alter organism responses to climate change.