Showing papers in "Earth System Dynamics Discussions in 2016"

Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C

Abstract: . Robust appraisals of climate impacts at different levels of global-mean temperature increase are vital to guide assessments of dangerous anthropogenic interference with the climate system. The 2015 Paris Agreement includes a two-headed temperature goal: "holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C". Despite the prominence of these two temperature limits, a comprehensive overview of the differences in climate impacts at these levels is still missing. Here we provide an assessment of key impacts of climate change at warming levels of 1.5 °C and 2 °C, including extreme weather events, water availability, agricultural yields, sea-level rise and risk of coral reef loss. Our results reveal substantial differences in impacts between a 1.5 °C and 2 °C warming that are highly relevant for the assessment of dangerous anthropogenic interference with the climate system. For heat-related extremes, the additional 0.5 °C increase in global-mean temperature marks the difference between events at the upper limit of present-day natural variability and a new climate regime, particularly in tropical regions. Similarly, this warming difference is likely to be decisive for the future of tropical coral reefs. In a scenario with an end-of-century warming of 2 °C, virtually all tropical coral reefs are projected to be at risk of severe degradation due to temperature-induced bleaching from 2050 onwards. This fraction is reduced to about 90 % in 2050 and projected to decline to 70 % by 2100 for a 1.5 °C scenario. Analyses of precipitation-related impacts reveal distinct regional differences and hot-spots of change emerge. Regional reduction in median water availability for the Mediterranean is found to nearly double from 9 % to 17 % between 1.5 °C and 2 °C, and the projected lengthening of regional dry spells increases from 7 to 11 %. Projections for agricultural yields differ between crop types as well as world regions. While some (in particular high-latitude) regions may benefit, tropical regions like West Africa, South-East Asia, as well as Central and northern South America are projected to face substantial local yield reductions, particularly for wheat and maize. Best estimate sea-level rise projections based on two illustrative scenarios indicate a 50 cm rise by 2100 relative to year 2000-levels for a 2 °C scenario, and about 10 cm lower levels for a 1.5 °C scenario. In a 1.5 °C scenario, the rate of sea-level rise in 2100 would be reduced by about 30 % compared to a 2 °C scenario. Our findings highlight the importance of regional differentiation to assess both future climate risks and different vulnerabilities to incremental increases in global-mean temperature. The article provides a consistent and comprehensive assessment of existing projections and a good basis for future work on refining our understanding of the difference between impacts at 1.5 °C and 2 °C warming.

Young People's Burden: Requirement of Negative CO2 Emissions

Abstract: . Global temperature is a fundamental climate metric highly correlated with sea level, which implies that keeping shorelines near their present location requires keeping global temperature within or close to its preindustrial Holocene range. However, global temperature excluding short-term variability now exceeds +1 °C relative to the 1880–1920 mean and annual 2016 global temperature was almost +1.3 °C. We show that global temperature has risen well out of the Holocene range and Earth is now as warm as it was during the prior (Eemian) interglacial period, when sea level reached 6–9 m higher than today. Further, Earth is out of energy balance with present atmospheric composition, implying that more warming is in the pipeline, and we show that the growth rate of greenhouse gas climate forcing has accelerated markedly in the past decade. The rapidity of ice sheet and sea level response to global temperature is difficult to predict, but is dependent on the magnitude of warming. Targets for limiting global warming thus, at minimum, should aim to avoid leaving global temperature at Eemian or higher levels for centuries. Such targets now require negative emissions , i.e., extraction of CO2 from the air. If phasedown of fossil fuel emissions begins soon, improved agricultural and forestry practices, including reforestation and steps to improve soil fertility and increase its carbon content, may provide much of the necessary CO2 extraction. In that case, the magnitude and duration of global temperature excursion above the natural range of the current interglacial (Holocene) could be limited and irreversible climate impacts could be minimized. In contrast, continued high fossil fuel emissions today place a burden on young people to undertake massive technological CO2 extraction if they are to limit climate change and its consequences. Proposed methods of extraction such as bioenergy with carbon capture and storage (BECCS) or air capture of CO2 have minimal estimated costs of USD 89–535 trillion this century and also have large risks and uncertain feasibility. Continued high fossil fuel emissions unarguably sentences young people to either a massive, implausible cleanup or growing deleterious climate impacts or both.

Hazard interactions and interaction networks (cascades) within multi-hazardmethodologies

Abstract: . This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability between successive hazards, and (iii) prioritise resource allocation for mitigation and disaster risk reduction.

Projections of leaf area index in earth system models

Abstract: . The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land–atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO2), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in some parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.

Geoengineering as a design problem

Abstract: . Understanding the climate impacts of solar geoengineering is essential for evaluating its benefits and risks. Most previous simulations have prescribed a particular strategy and evaluated its modeled effects. Here we turn this approach around by first choosing example climate objectives and then designing a strategy to meet those objectives in climate models. There are four essential criteria for designing a strategy: (i) an explicit specification of the objectives, (ii) defining what climate forcing agents to modify so the objectives are met, (iii) a method for managing uncertainties, and (iv) independent verification of the strategy in an evaluation model. We demonstrate this design perspective through two multi-objective examples. First, changes in Arctic temperature and the position of tropical precipitation due to CO2 increases are offset by adjusting high-latitude insolation in each hemisphere independently. Second, three different latitude-dependent patterns of insolation are modified to offset CO2-induced changes in global mean temperature, interhemispheric temperature asymmetry, and the Equator-to-pole temperature gradient. In both examples, the "design" and "evaluation" models are state-of-the-art fully coupled atmosphere–ocean general circulation models.

The Global Methane Budget: 2000–2012

Abstract: Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottomup estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30 N) compared to https://doi.org/10.5194/essd-12-1561-2020 Earth Syst. Sci. Data, 12, 1561–1623, 202

A novel bias correction methodology for climate impact simulations

Abstract: . Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome ), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes.

Accounting for the climate–carbon feedback in emission metrics

Abstract: . Most emission metrics have previously been inconsistently estimated by including the climate–carbon feedback for the reference gas (i.e. CO2) but not the other species (e.g. CH4). In the fifth assessment report of the IPCC, a first attempt was made to consistently account for the climate–carbon feedback in emission metrics. This attempt was based on only one study, and therefore the IPCC concluded that more research was needed. Here, we carry out this research. First, using the simple Earth system model OSCAR v2.2, we establish a new impulse response function for the climate–carbon feedback. Second, we use this impulse response function to provide new estimates for the two most common metrics: global warming potential (GWP) and global temperature-change potential (GTP). We find that, when the climate–carbon feedback is correctly accounted for, the emission metrics of non-CO2 species increase, but in most cases not as much as initially indicated by IPCC. We also find that, when the feedback is removed for both the reference and studied species, these relative metric values only have modest changes compared to when the feedback is included (absolute metrics change more markedly). Including or excluding the climate–carbon feedback ultimately depends on the user's goal, but consistency should be ensured in either case.

Atmospheric rivers moisture sources from a Lagrangian perspective

Abstract: . An automated atmospheric river (AR) detection algorithm is used for the North Atlantic Ocean basin, allowing the identification of the major ARs affecting western European coasts between 1979 and 2012 over the winter half-year (October to March). The entire western coast of Europe was divided into five domains, namely the Iberian Peninsula (9.75° W, 36–43.75° N), France (4.5° W, 43.75–50° N), UK (4.5° W, 50–59° N), southern Scandinavia and the Netherlands (5.25° E, 50–59° N), and northern Scandinavia (5.25° E, 59–70° N). Following the identification of the main ARs that made landfall in western Europe, a Lagrangian analysis was then applied in order to identify the main areas where the moisture uptake was anomalous and contributed to the ARs reaching each domain. The Lagrangian data set used was obtained from the FLEXPART (FLEXible PARTicle dispersion) model global simulation from 1979 to 2012 and was forced by ERA-Interim reanalysis on a 1° latitude–longitude grid. The results show that, in general, for all regions considered, the major climatological areas for the anomalous moisture uptake extend along the subtropical North Atlantic, from the Florida Peninsula (northward of 20° N) to each sink region, with the nearest coast to each sink region always appearing as a local maximum. In addition, during AR events the Atlantic subtropical source is reinforced and displaced, with a slight northward movement of the sources found when the sink region is positioned at higher latitudes. In conclusion, the results confirm not only the anomalous advection of moisture linked to ARs from subtropical ocean areas but also the existence of a tropical source, together with midlatitude anomaly sources at some locations closer to AR landfalls.

Hemispherically asymmetric volcanic forcing of tropical hydroclimate duringthe last millennium

Abstract: . Volcanic aerosols exert the most important natural radiative forcing of the last millennium. State-of-the-art paleoclimate simulations of this interval are typically forced with diverse spatial patterns of volcanic forcing, leading to different responses in tropical hydroclimate. Recently, theoretical considerations relating the intertropical convergence zone (ITCZ) position to the demands of global energy balance have emerged in the literature, allowing for a connection to be made between the paleoclimate simulations and recent developments in the understanding of ITCZ dynamics. These energetic considerations aid in explaining the well-known historical, paleoclimatic, and modeling evidence that the ITCZ migrates away from the hemisphere that is energetically deficient in response to asymmetric forcing. Here we use two separate general circulation model (GCM) suites of experiments for the last millennium to relate the ITCZ position to asymmetries in prescribed volcanic sulfate aerosols in the stratosphere and related asymmetric radiative forcing. We discuss the ITCZ shift in the context of atmospheric energetics and discuss the ramifications of transient ITCZ migrations for other sensitive indicators of changes in the tropical hydrologic cycle, including global streamflow. For the first time, we also offer insight into the large-scale fingerprint of water isotopologues in precipitation (δ18Op) in response to asymmetries in radiative forcing. The ITCZ shifts away from the hemisphere with greater volcanic forcing. Since the isotopic composition of precipitation in the ITCZ is relatively depleted compared to areas outside this zone, this meridional precipitation migration results in a large-scale enrichment (depletion) in the isotopic composition of tropical precipitation in regions the ITCZ moves away from (toward). Our results highlight the need for careful consideration of the spatial structure of volcanic forcing for interpreting volcanic signals in proxy records and therefore in evaluating the skill of Common Era climate model output.

Towards improved and more routine Earth system model evaluation in CMIP

Abstract: . The Coupled Model Intercomparison Project (CMIP) has successfully provided the climate community with a rich collection of simulation output from Earth system models (ESMs) that can be used to understand past climate changes and make projections and uncertainty estimates of the future. Confidence in ESMs can be gained because the models are based on physical principles and reproduce many important aspects of observed climate. More research is required to identify the processes that are most responsible for systematic biases and the magnitude and uncertainty of future projections so that more relevant performance tests can be developed. At the same time, there are many aspects of ESM evaluation that are well established and considered an essential part of systematic evaluation but have been implemented ad hoc with little community coordination. Given the diversity and complexity of ESM analysis, we argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently and consistently. Here, we provide a perspective and viewpoint on how a more systematic, open, and rapid performance assessment of the large and diverse number of models that will participate in current and future phases of CMIP can be achieved, and announce our intention to implement such a system for CMIP6. Accomplishing this could also free up valuable resources as many scientists are frequently "re-inventing the wheel" by re-writing analysis routines for well-established analysis methods. A more systematic approach for the community would be to develop and apply evaluation tools that are based on the latest scientific knowledge and observational reference, are well suited for routine use, and provide a wide range of diagnostics and performance metrics that comprehensively characterize model behaviour as soon as the output is published to the Earth System Grid Federation (ESGF). The CMIP infrastructure enforces data standards and conventions for model output and documentation accessible via the ESGF, additionally publishing observations (obs4MIPs) and reanalyses (ana4MIPs) for model intercomparison projects using the same data structure and organization as the ESM output. This largely facilitates routine evaluation of the ESMs, but to be able to process the data automatically alongside the ESGF, the infrastructure needs to be extended with processing capabilities at the ESGF data nodes where the evaluation tools can be executed on a routine basis. Efforts are already underway to develop community-based evaluation tools, and we encourage experts to provide additional diagnostic codes that would enhance this capability for CMIP. At the same time, we encourage the community to contribute observations and reanalyses for model evaluation to the obs4MIPs and ana4MIPs archives. The intention is to produce through the ESGF a widely accepted quasi-operational evaluation framework for CMIP6 that would routinely execute a series of standardized evaluation tasks. Over time, as this capability matures, we expect to produce an increasingly systematic characterization of models which, compared with early phases of CMIP, will more quickly and openly identify the strengths and weaknesses of the simulations. This will also reveal whether long-standing model errors remain evident in newer models and will assist modelling groups in improving their models. This framework will be designed to readily incorporate updates, including new observations and additional diagnostics and metrics as they become available from the research community.

Role of moisture transport for Central American precipitation

Abstract: . A climatology of moisture sources linked with Central American precipitation was computed based upon Lagrangian trajectories for the analysis period 1980–2013. The response of the annual cycle of precipitation in terms of moisture supply from the sources was analysed. Regional precipitation patterns are mostly driven by moisture transport from the Caribbean Sea (CS). Moisture supply from the eastern tropical Pacific (ETPac) and northern South America (NSA) exhibits a strong seasonal pattern but weaker compared to CS. The regional distribution of rainfall is largely influenced by a local signal associated with surface fluxes during the first part of the rainy season, whereas large-scale dynamics forces rainfall during the second part of the rainy season. The Caribbean Low Level Jet (CLLJ) and the Choco Jet (CJ) are the main conveyors of regional moisture, being key to define the seasonality of large-scale forced rainfall. Therefore, interannual variability of rainfall is highly dependent of the regional LLJs to the atmospheric variability modes. The El Nino–Southern Oscillation (ENSO) was found to be the dominant mode affecting moisture supply for Central American precipitation via the modulation of regional phenomena. Evaporative sources show opposite anomaly patterns during warm and cold ENSO phases, as a result of the strengthening and weakening, respectively, of the CLLJ during the summer months. Trends in both moisture supply and precipitation over the last three decades were computed, results suggest that precipitation trends are not homogeneous for Central America. Trends in moisture supply from the sources identified show a marked north–south seesaw, with an increasing supply from the CS Sea to northern Central America. Long-term trends in moisture supply are larger for the transition months (March and October). This might have important implications given that any changes in the conditions seen during the transition to the rainy season may induce stronger precipitation trends.

The role of spatial scale and background climate in the latitudinal temperature response to deforestation

Abstract: . Previous modeling and empirical studies have shown that the biophysical impact of deforestation is to warm the tropics and cool the extratropics. In this study, we use an earth system model of intermediate complexity to investigate how deforestation on various spatial scales affects ground temperature, with an emphasis on the latitudinal temperature response and its underlying mechanisms. Results show that the latitudinal pattern of temperature response depends nonlinearly on the spatial extent of deforestation and the fraction of vegetation change. Compared with regional deforestation, temperature change in global deforestation is greatly amplified in temperate and boreal regions but is dampened in tropical regions. Incremental forest removal leads to increasingly larger cooling in temperate and boreal regions, while the temperature increase saturates in tropical regions. The latitudinal and spatial patterns of the temperature response are driven by two processes with competing temperature effects: decrease in absorbed shortwave radiation due to increased albedo and decrease in evapotranspiration. These changes in the surface energy balance reflect the importance of the background climate in modifying the deforestation impact. Shortwave radiation and precipitation have an intrinsic geographical distribution that constrains the effects of biophysical changes and therefore leads to temperature changes that are spatially varying. For example, wet (dry) climate favors larger (smaller) evapotranspiration change; thus, warming (cooling) is more likely to occur. Our analysis reveals that the latitudinal temperature change largely results from the climate conditions in which deforestation occurs and is less influenced by the magnitude of individual biophysical changes such as albedo, roughness, and evapotranspiration efficiency.

Seasonality and spatial variability of dynamic precipitation controls on theTibetan Plateau

Abstract: . The Tibetan Plateau (TP) is the origin of many large Asian rivers, which provide water resources for large regions in south and east Asia. Therefore, the water cycle on the TP and adjacent high mountain ranges, in particular the precipitation distribution and variability play an important role for the water availability for billions of people in the downstream regions of the TP. The High Asia Refined analysis (HAR) is used to analyse the dynamical factors that influence precipitation variability in the TP region, including the factors resulting in the enhancement and suppression of precipitation. Four dynamical fields that can influence precipitation are considered: the 300 hPa wind speed and wind speed 2 km above ground, the 300 hPa vertical wind speed, and the atmospheric water transport. The study focusses on the seasonality and the spatial variability of the precipitation controls and their dominant patterns. Results show that different factors have different effects on precipitation in different regions and seasons. This depends mainly on the dominant type of precipitation, i.e. convective or frontal/cyclonic precipitation. Additionally, the study reveals that the midlatitude westerlies have a high impact on the precipitation distribution on the TP and its surroundings year-round and not only in winter.

Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics

Abstract: The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter This coupled behaviour causes various land–atmosphere feedbacks, and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections For example, a crucial role of the land surface in exacerbating summer heat waves in midlatitude regions has been identified empirically for high-impact heat waves, but individual climate models differ widely in their respective representation of land–atmosphere coupling Here, we compile an ensemble of 54 combinations of observations-based temperature (T) and evapotranspiration (ET) benchmarking datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land–atmosphere interactions during periods of anomalously warm temperatures First, we demonstrate that a large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in midlatitude regions during the warm season and in several tropical regions year-round These coincidences (high T, low ET) are closely related to the representation of temperature variability and extremes across the multi-model ensemble Second, we derive a land-coupling constraint based on the spread of the T–ET datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these benchmark estimates The constrained multi-model simulations exhibit more realistic temperature extremes of reduced magnitude in present climate in regions where models show substantial spread in T–ET coupling, ie biases in the model ensemble are consistently reduced Also the multi-model simulations for the coming decades display decreased absolute temperature extremes in the constrained ensemble On the other hand, the differences between projected and present-day climate extremes are affected to a lesser extent by the applied constraint, ie projected changes are reduced locally by around 05 to 1 °C – but this remains a local effect in regions that are highly sensitive to land–atmosphere coupling In summary, our approach offers a physically consistent, diagnostic-based avenue to evaluate multi-model ensembles and subsequently reduce model biases in simulated and projected extreme temperatures

Spatial and temporal variations in plant water-use efficiency inferred from tree-ring, eddy covariance and atmospheric observations

Abstract: . Plant water-use efficiency (WUE), which is the ratio of the uptake of carbon dioxide through photosynthesis to the loss of water through transpiration, is a very useful metric of the functioning of the land biosphere. WUE is expected to increase with atmospheric CO2, but to decline with increasing atmospheric evaporative demand – which can arise from increases in near-surface temperature or decreases in relative humidity. We have used Δ13C measurements from tree rings, along with eddy covariance measurements from Fluxnet sites, to estimate the sensitivities of WUE to changes in CO2 and atmospheric humidity deficit. This enables us to reconstruct fractional changes in WUE, based on changes in atmospheric climate and CO2, for the entire period of the instrumental global climate record. We estimate that overall WUE increased from 1900 to 2010 by 48 ± 22 %, which is more than double that simulated by the latest Earth System Models. This long-term trend is largely driven by increases in CO2, but significant inter-annual variability and regional differences are evident due to variations in temperature and relative humidity. There are several highly populated regions, such as western Europe and East Asia, where the rate of increase of WUE has declined sharply in the last 2 decades. Our data-based analysis indicates increases in WUE that typically exceed those simulated by Earth System Models – implying that these models are either underestimating increases in photosynthesis or underestimating reductions in transpiration.

Vegetation-climate feedbacks modulate rainfall patterns in Africa under future climate change

Abstract: . Africa has been undergoing significant changes in climate and vegetation in recent decades, and continued changes may be expected over this century. Vegetation cover and composition impose important influences on the regional climate in Africa. Climate-driven changes in vegetation structure and the distribution of forests versus savannah and grassland may feed back to climate via shifts in the surface energy balance, hydrological cycle and resultant effects on surface pressure and larger-scale atmospheric circulation. We used a regional Earth system model incorporating interactive vegetation–atmosphere coupling to investigate the potential role of vegetation-mediated biophysical feedbacks on climate dynamics in Africa in an RCP8.5-based future climate scenario. The model was applied at high resolution (0.44 × 0.44°) for the CORDEX-Africa domain with boundary conditions from the CanESM2 general circulation model. We found that increased tree cover and leaf-area index (LAI) associated with a CO2 and climate-driven increase in net primary productivity, particularly over subtropical savannah areas, not only imposed important local effect on the regional climate by altering surface energy fluxes but also resulted in remote effects over central Africa by modulating the land–ocean temperature contrast, Atlantic Walker circulation and moisture inflow feeding the central African tropical rainforest region with precipitation. The vegetation-mediated feedbacks were in general negative with respect to temperature, dampening the warming trend simulated in the absence of feedbacks, and positive with respect to precipitation, enhancing rainfall reduction over the rainforest areas. Our results highlight the importance of accounting for vegetation–atmosphere interactions in climate projections for tropical and subtropical Africa.

On the meaning of independence in climate science

Abstract: . The concept of independence has been frequently mentioned in climate science research, but has rarely been defined and discussed in a theoretically robust and quantifiable manner. In this paper we argue that any discussion must start from a clear and unambiguous definition of what independence means and how it can be determined. We introduce an approach based on the statistical definition of independence, and illustrate with simple examples how it can be applied to practical questions. Firstly, we apply these ideas to climate models, which are frequently argued to not be independent of each other, raising questions as to the robustness of results from multi-model ensembles. We explore the dependence between models in a multi-model ensemble, and suggest a possible way forward for future weighting strategies. Secondly, we discuss the issue of independence in relation to the synthesis of multiple observationally based constraints on the climate system, using equilibrium climate sensitivity as an example. We show that the same statistical theory applies to this problem, and illustrate this with a test case, indicating how researchers may estimate dependence between multiple constraints.

Scaling regimes and linear/nonlinear responses of last millennium climate to volcanic and solar forcings

Abstract: . At scales much longer than the deterministic predictability limits (about 10 days), the statistics of the atmosphere undergoes a drastic transition, the high-frequency weather acts as a random forcing on the lower-frequency macroweather. In addition, up to decadal and centennial scales the equivalent radiative forcings of solar, volcanic and anthropogenic perturbations are small compared to the mean incoming solar flux. This justifies the common practice of reducing forcings to radiative equivalents (which are assumed to combine linearly), as well as the development of linear stochastic models, including for forecasting at monthly to decadal scales. In order to clarify the validity of the linearity assumption and determine its scale range, we use last millennium simulations, with both the simplified Zebiak–Cane (ZC) model and the NASA GISS E2-R fully coupled GCM. We systematically compare the statistical properties of solar-only, volcanic-only and combined solar and volcanic forcings over the range of timescales from 1 to 1000 years. We also compare the statistics to multiproxy temperature reconstructions. The main findings are (a) that the variability in the ZC and GCM models is too weak at centennial and longer scales; (b) for longer than ≈ 50 years, the solar and volcanic forcings combine subadditively (nonlinearly) compounding the weakness of the response; and (c) the models display another nonlinear effect at shorter timescales: their sensitivities are much higher for weak forcing than for strong forcing (their intermittencies are different) and we quantify this with statistical scaling exponents.

Groundwater nitrate concentration evolution under climate change and agricultural adaptation scenarios: Prince Edward Island, Canada

Abstract: . Nitrate (N-NO3) concentration in groundwater, the sole source of potable water in Prince Edward Island (PEI, Canada), currently exceeds the 10 mg L−1 (N-NO3) health threshold for drinking water in 6 % of domestic wells. Increasing climatic and socio-economic pressures on PEI agriculture may further deteriorate groundwater quality. This study assesses how groundwater nitrate concentration could evolve due to the forecasted climate change and its related potential changes in agricultural practices. For this purpose, a tridimensional numerical groundwater flow and mass transport model was developed for the aquifer system of the entire Island (5660 km2). A number of different groundwater flow and mass transport simulations were made to evaluate the potential impact of the projected climate change and agricultural adaptation. According to the simulations for year 2050, N-NO3 concentration would increase due to two main causes: (1) the progressive attainment of steady-state conditions related to present-day nitrogen loadings, and (2) the increase in nitrogen loadings due to changes in agricultural practices provoked by future climatic conditions. The combined effects of equilibration with loadings, climate and agricultural adaptation would lead to a 25 to 32 % increase in N-NO3 concentration over the Island aquifer system. The change in groundwater recharge regime induced by climate change (with current agricultural practices) would only contribute 0 to 6 % of that increase for the various climate scenarios. Moreover, simulated trends in groundwater N-NO3 concentration suggest that an increased number of domestic wells (more than doubling) would exceed the nitrate drinking water criteria. This study underlines the need to develop and apply better agricultural management practices to ensure sustainability of long-term groundwater resources. The simulations also show that observable benefits from positive changes in agricultural practices would be delayed in time due to the slow dynamics of nitrate transport within the aquifer system.

Climate engineering by mimicking natural dust climate control: the iron salt aerosol method

Abstract: . Power stations, ships and air traffic are among the most potent greenhouse gas emitters and are primarily responsible for global warming. Iron salt aerosols (ISAs), composed partly of iron and chloride, exert a cooling effect on climate in several ways. This article aims firstly to examine all direct and indirect natural climate cooling mechanisms driven by ISA tropospheric aerosol particles, showing their cooperation and interaction within the different environmental compartments. Secondly, it looks at a proposal to enhance the cooling effects of ISA in order to reach the optimistic target of the Paris climate agreement to limit the global temperature increase between 1.5 and 2 °C. Mineral dust played an important role during the glacial periods; by using mineral dust as a natural analogue tool and by mimicking the same method used in nature, the proposed ISA method might be able to reduce and stop climate warming. The first estimations made in this article show that by doubling the current natural iron emissions by ISA into the troposphere, i.e., by about 0.3 Tg Fe yr−1, artificial ISA would enable the prevention or even reversal of global warming. The ISA method proposed integrates technical and economically feasible tools.

Revolutions in energy input and material cycling in Earth history and human history

Abstract: . Major revolutions in energy capture have occurred in both Earth and human history, with each transition resulting in higher energy input, altered material cycles and major consequences for the internal organization of the respective systems. In Earth history, we identify the origin of anoxygenic photosynthesis, the origin of oxygenic photosynthesis, and land colonization by eukaryotic photosynthesizers as step changes in free energy input to the biosphere. In human history we focus on the Palaeolithic use of fire, the Neolithic revolution to farming, and the Industrial revolution as step changes in free energy input to human societies. In each case we try to quantify the resulting increase in energy input, and discuss the consequences for material cycling and for biological and social organization. For most of human history, energy use by humans was but a tiny fraction of the overall energy input to the biosphere, as would be expected for any heterotrophic species. However, the industrial revolution gave humans the capacity to push energy inputs towards planetary scales and by the end of the 20th century human energy use had reached a magnitude comparable to the biosphere. By distinguishing world regions and income brackets we show the unequal distribution in energy and material use among contemporary humans. Looking ahead, a prospective sustainability revolution will require scaling up new renewable and decarbonized energy technologies and the development of much more efficient material recycling systems – thus creating a more autotrophic social metabolism. Such a transition must also anticipate a level of social organization that can implement the changes in energy input and material cycling without losing the large achievements in standard of living and individual liberation associated with industrial societies.

Uncertainties in the land use flux resulting from land use change reconstructions and gross land transitions

Abstract: . Land-use and land-cover (LUC) changes are a key uncertainty when attributing changes in measured atmospheric CO2 concentration to its sinks and sources and must also be much better understood to determine the possibilities for land-based climate change mitigation, especially in the light of human demand on other land-based resources. On the spatial scale typically used in terrestrial ecosystem models (0.5 or 1°) changes in LUC over time periods of a few years or more can include bidirectional changes on the sub-grid level, such as the parallel expansion and abandonment of agricultural land (e.g. in shifting cultivation) or cropland–grassland conversion (and vice versa). These complex changes between classes within a grid cell have often been neglected in previous studies, and only net changes of land between natural vegetation cover, cropland and pastures accounted for, mainly because of a lack of reliable high-resolution historical information on gross land transitions, in combination with technical limitations within the models themselves. In the present study we applied a state-of-the-art dynamic global vegetation model with a detailed representation of croplands and carbon–nitrogen dynamics to quantify the uncertainty in terrestrial ecosystem carbon stocks and fluxes arising from the choice between net and gross representations of LUC. We used three frequently applied global, one recent global and one recent European LUC datasets, two of which resolve gross land transitions, either in Europe or in certain tropical regions. When considering only net changes, land-use-transition uncertainties (expressed as 1 standard deviation around decadal means of four models) in global carbon emissions from LUC (ELUC) are ±0.19, ±0.66 and ±0.47 Pg C a−1 in the 1980s, 1990s and 2000s, respectively, or between 14 and 39 % of mean ELUC. Carbon stocks at the end of the 20th century vary by ±11 Pg C for vegetation and ±37 Pg C for soil C due to the choice of LUC reconstruction, i.e. around 3 % of the respective C pools. Accounting for sub-grid (gross) land conversions significantly increased the effect of LUC on global and European carbon stocks and fluxes, most noticeably enhancing global cumulative ELUC by 33 Pg C (1750–2014) and entailing a significant reduction in carbon stored in vegetation, although the effect on soil C stocks was limited. Simulations demonstrated that assessments of historical carbon stocks and fluxes are highly uncertain due to the choice of LUC reconstruction and that the consideration of different contrasting LUC reconstructions is needed to account for this uncertainty. The analysis of gross, in addition to net, land-use changes showed that the full complexity of gross land-use changes is required in order to accurately predict the magnitude of LUC change emissions. This introduces technical challenges to process-based models and relies on extensive information regarding historical land-use transitions.

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

Abstract: . Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

Sustainable use of renewable resources in a stylized social–ecological network model under heterogeneous resource distribution

Abstract: . Human societies depend on the resources ecosystems provide. Particularly since the last century, human activities have transformed the relationship between nature and society at a global scale. We study this coevolutionary relationship by utilizing a stylized model of private resource use and social learning on an adaptive network. The latter process is based on two social key dynamics beyond economic paradigms: boundedly rational imitation of resource use strategies and homophily in the formation of social network ties. The private and logistically growing resources are harvested with either a sustainable (small) or non-sustainable (large) effort. We show that these social processes can have a profound influence on the environmental state, such as determining whether the private renewable resources collapse from overuse or not. Additionally, we demonstrate that heterogeneously distributed regional resource capacities shift the critical social parameters where this resource extraction system collapses. We make these points to argue that, in more advanced coevolutionary models of the planetary social–ecological system, such socio-cultural phenomena as well as regional resource heterogeneities should receive attention in addition to the processes represented in established Earth system and integrated assessment models.

The Southern Ocean as a constraint to reduce uncertainty in future ocean carbon sinks

Abstract: . Earth system model (ESM) simulations exhibit large biases compares to observation-based estimates of the present ocean CO2 sink. The inter-model spread in projections increases nearly 2-fold by the end of the 21st century and therefore contributes significantly to the uncertainty of future climate projections. In this study, the Southern Ocean (SO) is shown to be one of the hot-spot regions for future uptake of anthropogenic CO2, characterized by both the solubility pump and biologically mediated carbon drawdown in the spring and summer. We show, by analyzing a suite of fully interactive ESMs simulations from the Coupled Model Intercomparison Project phase 5 (CMIP5) over the 21st century under the high-CO2 Representative Concentration Pathway (RCP) 8.5 scenario, that the SO is the only region where the atmospheric CO2 uptake rate continues to increase toward the end of the 21st century. Furthermore, our study discovers a strong inter-model link between the contemporary CO2 uptake in the Southern Ocean and the projected global cumulated uptake over the 21st century. This strong correlation suggests that models with low (high) carbon uptake rate in the contemporary SO tend to simulate low (high) uptake rate in the future. Nevertheless, our analysis also shows that none of the models fully capture the observed biophysical mechanisms governing the CO2 fluxes in the SO. The inter-model spread for the contemporary CO2 uptake in the Southern Ocean is attributed to the variations in the simulated seasonal cycle of surface pCO2. Two groups of model behavior have been identified. The first one simulates anomalously strong SO carbon uptake, generally due to both too strong a net primary production and too low a surface pCO2 in December–January. The second group simulates an opposite CO2 flux seasonal phase, which is driven mainly by the bias in the sea surface temperature variability. We show that these biases are persistent throughout the 21st century, which highlights the urgent need for a sustained and comprehensive biogeochemical monitoring system in the Southern Ocean to better constrain key processes represented in current model systems.

The impact of structural error on parameter constraint in a climate model

Abstract: . Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections of future climate change. We use observations of forest fraction to constrain carbon cycle and land surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error. Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the climate model, we use history matching to rule out input parameter settings where the corresponding climate model output is judged sufficiently different from observations, even allowing for uncertainty. Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to tune the simulator leads to a larger underestimate of the Amazon forest fraction. We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the simulated Amazon, including missing processes in the land surface component and a bias in the climatology of the Amazon.

Seasonal effects of irrigation on land–atmosphere latent heat, sensible heat, and carbon fluxes in semiarid basin

Abstract: . Irrigation, which constitutes ∼ 70 % of the total amount of freshwater consumed by the human population, is significantly impacting land–atmosphere fluxes. In this study, using the improved Community Land Model version 4.5 (CLM4.5) with an active crop model, two high-resolution (∼ 1 km) simulations investigating the effects of irrigation on latent heat (LH), sensible heat (SH), and carbon fluxes (or net ecosystem exchange, NEE) from land to atmosphere in the Heihe River basin in northwestern China were conducted using a high-quality irrigation dataset compiled from 1981 to 2013. The model output and measurements from remote sensing demonstrated the capacity of the developed models to reproduce ecological and hydrological processes. The results revealed that the effects of irrigation on LH and SH are strongest during summer, with a LH increase of ∼ 100 W m−2 and a SH decrease of ∼ 60 W m−2 over intensely irrigated areas. However, the reactions are much weaker during spring and autumn when there is much less irrigation. When the irrigation rate is below 5 mm day−1, the LH generally increases, whereas the SH decreases with growing irrigation rates. However, when the irrigation threshold is in excess of 5 mm day−1, there is no accrued effect of irrigation on the LH and SH. Irrigation produces opposite effects to the NEE during spring and summer. During the spring, irrigation yields more discharged carbon from the land to the atmosphere, increasing the NEE value by 0.4–0.8 gC m−2 day−1, while the summer irrigation favors crop fixing of carbon from atmospheric CO2, decreasing the NEE value by ∼ 0.8 gC m−2 day−1. The repercussions of irrigation on land–atmosphere fluxes are not solely linked to the irrigation amount, and other parameters (especially the temperature) also control the effects of irrigation on LH, SH, and NEE.

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

Abstract: . We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) using socio-economic data from the SSPs and climate data from the RCPs (representative concentration pathways). The simulated range of global cropland is 893–2380 Mha in 2100 (± 1 standard deviation), with the main uncertainties arising from differences in the socio-economic conditions prescribed by the SSP scenarios and the assumptions that underpin the translation of qualitative SSP storylines into quantitative model input parameters. Uncertainties in the assumptions for population growth, technological change and cropland degradation were found to be the most important for global cropland, while uncertainty in food consumption had less influence on the results. The uncertainties arising from climate variability and the differences between climate change scenarios do not strongly affect the range of global cropland futures. Some overlap occurred across all of the conditional probabilistic futures, except for those based on SSP3. We conclude that completely different socio-economic and climate change futures, although sharing low to medium population development, can result in very similar cropland areas on the aggregated global scale.

Horses for courses : analytical tools to explore planetary boundaries

Abstract: . There is a need for more integrated research on sustainable development and global environmental change. In this paper, we focus on the planetary boundaries framework to provide a systematic categorization of key research questions in relation to avoiding severe global environmental degradation. The four categories of key questions are those that relate to (1) the underlying processes and selection of key indicators for planetary boundaries, (2) understanding the impacts of environmental pressure and connections between different types of impacts, (3) better understanding of different response strategies to avoid further degradation, and (4) the available instruments to implement such strategies. Clearly, different categories of scientific disciplines and associated model types exist that can accommodate answering these questions. We identify the strength and weaknesses of different research areas in relation to the question categories, focusing specifically on different types of models. We discuss that more interdisciplinary research is need to increase our understanding by better linking human drivers and social and biophysical impacts. This requires better collaboration between relevant disciplines (associated with the model types), either by exchanging information or by fully linking or integrating them. As fully integrated models can become too complex, the appropriate type of model (the racehorse) should be applied for answering the target research question (the race course).