Search

The mitigation potential of vegetation-driven biophysical effects is strongly influenced by the background climate and will therefore be influenced by global warming. Based on an ensemble of remote sensing datasets, here we first estimate the temperature sensitivities to changes in leaf area over the period 2003–2014 as a function of key environmental drivers. These sensitivities are then used to predict temperature changes induced by future leaf area dynamics under four scenarios. Results show that by 2100, under high-emission scenario, greening will likely mitigate land warming by 0.71 ± 0.40 °C, and 83% of such effect (0.59 ± 0.41 °C) is driven by the increase in plant carbon sequestration, while the remaining cooling (0.12 ± 0.05 °C) is due to biophysical land-atmosphere interactions. In addition, our results show a large potential of vegetation to reduce future land warming in the very-stringent scenario (35 ± 20% of the overall warming signal), whereas this effect is limited to 11 ± 6% under the high-emission scenario., Vegetation changes have been suggested as a climate mitigation option, but the numerous feedbacks between vegetation and climate are not well understood. Here, the authors show that greening leads to surface cooling in many areas, but the size of the effect depends on the background climate.

Managing transboundary river basins requires balancing tradeoffs of sustainable water use and coping with climate uncertainty. We demonstrate an integrated approach to exploring these issues through the lens of a social-ecological system, combining remote and in-situ earth observations, hydrologic and climate models, and social surveys. Specifically, we examine how climate change and dam development could impact the Se Kong, Se San and Sre Pok rivers in the Mekong region. We find that climate change will lead to increased precipitation, necessitating a shift in dam operations, from maintaining low flows to reducing flood hazards. We also find that existing water governance systems in Laos, Vietnam, and Cambodia are ill-prepared to address the problem. We conclude that the solution space for addressing these complex issues will be highly constrained unless major deficiencies in transboundary water governance, strategic planning, financial capacity, information sharing, and law enforcement are remedied in the next decades.

European Union funding: 821205, 821003, 820829. Wellcome Trust: 205212/Z/16/Z

Larger organised convective storms (mesoscale-convective systems) can lead to major flood events in Europe. Here we assess end-of-century changes to their characteristics in two convection-permitting climate simulations from the UK Met Office and ETH-Zürich that both use the high Representative Concentration Pathway 8.5 scenario but different approaches to represent atmospheric changes with global warming and different models. The UK Met Office projections indicate more frequent, smaller, and slower-moving storms, while ETH-Zürich projections show fewer, larger, and faster-moving storms. However, both simulations show increases to peak precipitation intensity, total precipitation volume, and temporal clustering, suggesting increasing risks from mesoscale-convective systems in the future. Importantly, the largest storms that pose increased flood risks are projected to increase in frequency and intensity. These results highlight that understanding large-scale dynamical drivers as well as the thermodynamical response of storms is essential for accurate projections of changes to storm hazards, needed for future climate adaptation., Communications Earth & Environment, 4 (1), ISSN:2662-4435

Robust detection of anthropogenic climate change is crucial to: (i) improve our understanding of Earth system responses to external forcing, (ii) reduce uncertainty in future climate projections, and (iii) develop efficient mitigation and adaptation plans. Here, we use Earth system model projections to establish the detection timescales of anthropogenic signals in the global ocean through analyzing temperature, salinity, oxygen, and pH evolution from surface to 2000 m depths. For most variables, anthropogenic changes emerge earlier in the interior ocean than at the surface, due to the lower background variability at depth. Acidification is detectable earliest, followed by warming and oxygen changes in the subsurface tropical Atlantic. Temperature and salinity changes in the subsurface tropical and subtropical North Atlantic are shown to be early indicators for a slowdown of the Atlantic Meridional Overturning Circulation. Even under mitigated scenarios, inner ocean anthropogenic signals are projected to emerge within the next few decades. This is because they originate from existing surface changes that are now propagating into the interior. In addition to the tropical Atlantic, our study calls for establishment of long-term interior monitoring systems in the Southern Ocean and North Atlantic in order to elucidate how spatially heterogeneous anthropogenic signals propagate into the interior and impact marine ecosystems and biogeochemistry.

As the Arctic continues to warm faster than the rest of the planet, evidence mounts that the region is experiencing unprecedented environmental change. The hydrological cycle is projected to intensify throughout the twenty-first century, with increased evaporation from expanding open water areas and more precipitation. The latest projections from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) point to more rapid Arctic warming and sea-ice loss by the year 2100 than in previous projections, and consequently, larger and faster changes in the hydrological cycle. Arctic precipitation (rainfall) increases more rapidly in CMIP6 than in CMIP5 due to greater global warming and poleward moisture transport, greater Arctic amplification and sea-ice loss and increased sensitivity of precipitation to Arctic warming. The transition from a snow- to rain-dominated Arctic in the summer and autumn is projected to occur decades earlier and at a lower level of global warming, potentially under 1.5 °C, with profound climatic, ecosystem and socio-economic impacts., The Arctic warms faster than other areas of the planet, which also influences precipitation. Here, the authors show that the latest CMIP6 model ensemble shows a faster Arctic warming and sea-ice loss, causing an earlier transition from a snow- to a rain-dominated Arctic than previously thought.

The Southern Ocean exerts a strong influence on global climate, regulating the storage and transport of heat, freshwater and carbon throughout the world’s oceans. While the majority of previous studies focus on how wind changes influence Southern Ocean circulation patterns, here we set out to explore potential feedbacks from the ocean to the atmosphere. To isolate the role of oceanic variability on Southern Hemisphere climate, we perform coupled climate model experiments in which Southern Ocean variability is suppressed by restoring sea surface temperatures (SST) over 40°–65°S to the model’s monthly mean climatology. We find that suppressing Southern Ocean SST variability does not impact the Southern Annular Mode, suggesting air–sea feedbacks do not play an important role in the persistence of the Southern Annular Mode in our model. Suppressing Southern Ocean SST variability does lead to robust mean-state changes in SST and sea ice. Changes in mixed layer processes and convection associated with the SST restoring lead to SST warming and a sea ice decline in southern high latitudes, and SST cooling in midlatitudes. These results highlight the impact non-linear processes can have on a model’s mean state, and the need to consider these when performing simulations of the Southern Ocean.

Recent climate extremes have broken long-standing records by large margins. Such extremes unprecedented in the observational period often have substantial impacts due to a tendency to adapt to the highest intensities, and no higher, experienced during a lifetime. Here, we show models project not only more intense extremes but also events that break previous records by much larger margins. These record-shattering extremes, nearly impossible in the absence of warming, are likely to occur in the coming decades. We demonstrate that their probability of occurrence depends on warming rate, rather than global warming level, and is thus pathway-dependent. In high-emission scenarios, week-long heat extremes that break records by three or more standard deviations are two to seven times more probable in 2021–2050 and three to 21 times more probable in 2051–2080, compared to the last three decades. In 2051–2080, such events are estimated to occur about every 6–37 years somewhere in the northern midlatitudes. Changes in extreme heat are often calculated as anomalies above a reference climatology. A different definition—week-day heatwaves surpassing the current record by large margins—shows that their occurrence probabilities depend on warming rate, not level, and are higher than during recent decades.

The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation., Climate models project an intensification of extreme precipitation under climate change, but this effect is difficult to detect in the observational record. Here, the authors show that a physically interpretable anthropogenic impact on extreme precipitation is detectable in global observational data sets.

Migratory cyclones and anticyclones account for most of the day-to-day weather variability in the extratropics. These transient eddies act to maintain the midlatitude jet streams by systematically transporting westerly momentum and heat. Yet, little is known about the separate contributions of cyclones and anticyclones to their interaction with the westerlies. Here, using a novel methodology for identifying cyclonic and anticyclonic vortices based on curvature, we quantify their separate contributions to atmospheric energetics and their feedback on the westerly jet streams as represented in Eulerian statistics. We show that climatological westerly acceleration by cyclonic vortices acts to dominantly reinforce the wintertime eddy-driven near-surface westerlies and associated cyclonic shear. Though less baroclinic and energetic, anticyclones still play an important role in transporting westerly momentum toward midlatitudes from the upper-tropospheric thermally driven jet core and carrying eddy energy downstream. These new findings have uncovered essential characteristics of atmospheric energetics, storm track dynamics and eddy-mean flow interaction.

Climate model simulations that restore SSTs in the North Atlantic have been used to explore the climate impacts of Atlantic Multidecadal Variability (AMV). However, despite simulations and observations exhibiting similar North Atlantic SST anomalies, experiments with active SST-restoring in the Tropical North Atlantic exhibit strong positive surface heat-fluxes out of the ocean with warm SST anomalies, which is not replicated in other simulations or observations. The upward surface heat-fluxes that are systematically driven by the active SST-restoring in the Tropical North Atlantic are found to be crucial for generating a strong local precipitation response and the associated remote impact on the Pacific Walker circulation; these are both absent in other simulations. The results of this study strongly suggest that experiments employing SST-restoring (or prescribed SSTs) in the Tropical North Atlantic exaggerate the influence of the Atlantic on patterns of global climate anomalies and its role in recent multidecadal SST trends. COR was supported by a Royal Society University Research Fellowship. MP was funded by the EUCP project (Horizon 2020; Grant Agreement 776613). JR was funded by NERC via the ACSIS program (NE/N018001/1) and via the WISHBONE project (NE/T013516/1). PAM was funded by the EMERGENCE project under the Natural Environment Research Council (NERC Grant NE/S004890/1) The project that gave rise to these results included YRR and received the support of a fellowship from “la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648 (fellowship code LCF/BQ/PR21/11840016).

Producing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent., The Madden-Julian Oscillation (MJO) is a crucial component of the tropical weather system, but forecasting it has been challenging. Here, the authors present a deep learning bias correction method that significantly improves multi-model forecasts of the MJO amplitude and phase for up to four weeks.

Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation., Terrestrial water storage (TWS) modulates the hydrological cycle and is a key determinant of water availability and an indicator of drought. While historical TWS variations have been increasingly studied, future changes in TWS and the linkages to droughts remain unexamined. Here, using ensemble hydrological simulations, we show that climate change could reduce TWS in many regions, especially those in the Southern Hemisphere. Strong inter-ensemble agreement indicates high confidence in the projected changes that are driven primarily by climate forcing rather than land and water management activities. Declines in TWS translate to increases in future droughts. By the late twenty-first century, the global land area and population in extreme-to-exceptional TWS drought could more than double, each increasing from 3% during 1976–2005 to 7% and 8%, respectively. Our findings highlight the importance of climate change mitigation to avoid adverse TWS impacts and increased droughts, and the need for improved water resource management and adaptation. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

Enhancing crop production, particularly by growing a crop in the typically-fallow dry season is a key strategy for alleviating poverty in the Ganges delta region. We used a polder water and salt balance model to examine the impact of several crop management, salt management and climate change scenarios on salinity and crop evapotranspiration at Dacope and Amtali in Bangladesh and Gosaba in India. A key (and unsurprising) finding is that salt management is very important, particularly at the two drier sites, Dacope and Gosaba. Good salt management lowers salinity in the shallow groundwater, soil and water storage ponds, and leads to more irrigation. Climate change is projected to alter rainfall, and this in turn leads to modelled increases or decreases in runoff from the polders, and thence affect salt concentrations in the soil and ponds and canals. Thus, the main impacts of climate change are through the indirect impacts on salt concentrations, rather than the direct impacts of the amount of water supplied as rainfall. Management practices to remove salt from polders are therefore likely to be effective in combatting the impacts of projected climate change particularly at Dacope and Gosaba.

Regional climate models (RCMs) are indispensable in climate research, albeit often characterized by biased terrestrial precipitation and water budgets. This study identifies excess oceanic evaporation, in conjunction with the RCMs’ boundary conditions, as drivers contributing to these biases in RCMs with forced sea surface temperatures in a CORDEX RCM ensemble over Europe. The RCMs are relaxed to the prescribed lateral boundary conditions originating from a global model, effectively matching the driving model's overall atmospheric moisture flux divergence. As a consequence, excess oceanic evaporation results in positive precipitation biases over land due to forced internal recycling of moisture to maintain the overall flux divergence prescribed by the boundary conditions. This systematic behaviour is shown through an analysis of long-term atmospheric water budgets and atmospheric moisture exchange between oceanic and continental areas in a multi-model ensemble.

The Beaufort Gyre (BG), the largest Arctic Ocean freshwater reservoir, has drastically increased its liquid freshwater content by 40% in the past two decades. If released within a short period, the excess freshwater could potentially impact the large-scale ocean circulation by freshening the upper subpolar North Atlantic. Here, we track BG-sourced freshwater using passive tracers in a global ocean sea-ice model and show that this freshwater exited the Arctic mostly through the Canadian Arctic Archipelago, rather than Fram Strait, during an historical release event in 1983–1995. The Labrador Sea is the most affected region in the subpolar North Atlantic, with a freshening of 0.2 psu on the western shelves and 0.4 psu in the Labrador Current. Given that the present BG freshwater content anomaly is twice the historical analog studied here, the impact of a future rapid release on Labrador Sea salinity could be significant, easily exceeding similar fluxes from Greenland meltwater., The Beaufort Gyre in the western Arctic Ocean has accumulated a large amount of freshwater. Here, the authors show that a historical release in the 1980s resulted in a strong freshening of the western Labrador Sea, suggesting that a future release of the current freshwater volume could even be more impactful.

A robust eastern Pacific surface temperature cooling trend was evident between ~1990–2013 that was considered as a pronounced contributor to the global surface warming slowdown. The majority of current climate models failed to reproduce this Pacific cooling trend, which is at least partly due to the underrepresentation of trans-basin teleconnections. Here, we investigate whether common Pacific mean sea surface temperature biases may further diminish the Atlantic-Pacific trans-basin induced Pacific cooling. Our results suggest that background Pacific SST biases act to weaken the trans-basin teleconnection by strengthening the Atlantic atmospheric stability and reducing Atlantic convection. These Pacific SST biases also act to substantially undermine the positive zonal wind-SST feedback. Furthermore, when combined, the Pacific and Atlantic SST biases led to Pacific cooling response that is almost non-existent (underestimated by 89%). Future efforts aim at reducing the model mean state biases may significantly help to improve the simulation skills of trans-basin teleconnections., Many climate models failed to reproduce the eastern Pacific cooling that has been linked to slower warming in the early 20th century. Here, the authors present a feedback mechanism between the tropical Pacific and the Atlantic which contributes to this bias as it further dampens the Pacific cooling response in models.

Coral reefs are under increasingly severe threat from climate change and other anthropogenic stressors. Anomalously high seawater temperatures in particular are known to cause coral bleaching (loss of algal symbionts in the family Symbiodiniaceae), which frequently leads to coral mortality. Remote sensing of sea surface temperature (SST) has served as an invaluable tool for monitoring physical conditions that can lead to bleaching events over relatively large scales (e.g. few kms to 100 s of kms). But, it is also well known that seawater temperatures within a site can vary significantly across depths due to the combined influence of solar heating of surface waters, water column thermal stratification, and cooling from internal waves and upwelling. We deployed small autonomous benthic temperature sensors at depths ranging from 0–40 m in fore reef, back reef, and lagoonal reef habitats on the Belize Mesoamerican Barrier Reef System from 2000–2019. These data can be used to calculate depth-specific climatologies across reef depths and sites, and emphasize the dynamic and spatially-variable nature of coral reef physical environments., Measurement(s) temperature of water Technology Type(s) Temperature Sensor Device Factor Type(s) temporal interval • geographic location • depth Sample Characteristic - Environment sea • marine reef • coral reef • marine coral reef back reef • marine coral reef fore reef Sample Characteristic - Location Belize Barrier Reef Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13095506

Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05° × 0.05°) and spreads under a more comprehensive set of SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. We compared our dataset with the Land Use Harmonization version 2 (LUH2) dataset and found our results are in general spatially consistent with LUH2. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts., Measurement(s) land use process • global land use and land cover Technology Type(s) computational modeling technique Factor Type(s) year of data collection • Shared Socioeconomic Pathways scenarios • Representative Concentration Pathways scenarios Sample Characteristic - Environment land Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12967760

Several large-scale cryosphere elements such as the Arctic summer sea ice, the mountain glaciers, the Greenland and West Antarctic Ice Sheet have changed substantially during the last century due to anthropogenic global warming. However, the impacts of their possible future disintegration on global mean temperature (GMT) and climate feedbacks have not yet been comprehensively evaluated. Here, we quantify this response using an Earth system model of intermediate complexity. Overall, we find a median additional global warming of 0.43 °C (interquartile range: 0.39−0.46 °C) at a CO2 concentration of 400 ppm. Most of this response (55%) is caused by albedo changes, but lapse rate together with water vapour (30%) and cloud feedbacks (15%) also contribute significantly. While a decay of the ice sheets would occur on centennial to millennial time scales, the Arctic might become ice-free during summer within the 21st century. Our findings imply an additional increase of the GMT on intermediate to long time scales., The disintegration of cryosphere elements such as the Arctic summer sea ice, mountain glaciers, Greenland and West Antarctica is associated with temperature and radiative feedbacks. In this work, the authors quantify these feedbacks and find an additional global warming of 0.43°C.

Approaches that root national climate strategies in local actions will be essential for all countries as they develop new nationally determined contributions under the Paris Agreement. The potential impact of climate action from non-national actors in delivering higher global ambition is significant. Sub-national action in the United States provides a test for how such actions can accelerate emissions reductions. We aggregated U.S. state, city, and business commitments within an integrated assessment model to assess how a national climate strategy can be built upon non-state actions. We find that existing commitments alone could reduce emissions 25% below 2005 levels by 2030, and that enhancing actions by these actors could reduce emissions up to 37%. We show how these actions can provide a stepped-up basis for additional federal action to reduce emissions by 49%—consistent with 1.5 °C. Our analysis demonstrates sub-national actions can lead to substantial reductions and support increased national action., Climate action from local actors is vital in achieving nationally determined contributions under the Paris Agreement. Here the authors show that existing commitments from U.S. states, cities and business could reduce emissions 25% below 2005 levels by 2030, with expanded subnational action reducing emissions by 37% and federal action by up to 49%.

Despite the observed monotonic increase in greenhouse-gas concentrations, global mean temperature displays important decadal fluctuations typically attributed to both external forcing and internal variability. Here, we provide a robust quantification of the relative contributions of anthropogenic, natural, and internally-driven decadal variability of global mean sea surface temperature (GMSST) by using a unique dataset consisting of 30-member large initial-condition ensembles with five Earth System Models (ESM-LE). We present evidence that a large fraction (~29–53%) of the simulated decadal-scale variance in individual timeseries of GMSST over 1950–2010 is externally forced and largely linked to the representation of volcanic aerosols. Comparison with the future (2010–2070) period suggests that external forcing provides a source of additional decadal-scale variability in the historical period. Given the unpredictable nature of future volcanic aerosol forcing, it is suggested that a large portion of decadal GMSST variability might not be predictable., Global mean sea surface surface temperature shows decadal fluctuations superimposed to the warming trend whose causes are still debated. Here, the authors provide a quantification of relative contributions of different drivers and conclude that both internal and externally-forced variability play a comparable role.

Various proxies suggest a nearly in-phase variation of monsoons with local summer insolation. Oceanic proxies of monsoons document a more complex response. Climate model simulations also indicate that the response is different over land and ocean. Here using a transient simulation by a climate model over the last 22,000 years we have unraveled the factors that lead to these differences within the Indian subcontinent. We show that during the deglacial (22–12 ka) precipitation over India and the Bay of Bengal (BoB) are in phase, whereas they are out of phase across the Holocene (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼ 12 ka to 0 ka). During the deglacial, water vapor amplifies the effect of solar forcing on precipitation over both the regions, whereas contributions from surface latent heat fluxes over the BoB drive an opposite response across the Holocene. We find that greenhouse gas forcing drives similar precipitation response over land and ocean, whereas orbital forcing produces a different response over land and ocean. We have further demonstrated that during periods of abrupt climate change [such as the Bølling–Allerød (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼ 14 ka)], water vapor affects precipitation mainly through its influence on the vertical stability of the atmosphere. These results highlight the complex nature of precipitation over the BoB and thus has implications for the interpretation of monsoon proxies.

Carbonaceous aerosols, which are emitted from biomass burning, significantly contribute to the Earth’s radiation balance. Radiative forcing caused by biomass burning has been poorly qualified, which is largely attributed to uncertain absorption enhancement values (Eabs) of black carbon (BC) aerosols. Laboratory measurements and theoretical modelling indicate a significant value of Eabs; but this enhancement is observed to be negligible in the ambient environment, implying that models may overestimate global warming due to BC. Here, we present an aggregate model integrating BC aerosol ensembles with different morphologies and mixing states and report a quantitative analysis of the BC Eabs from different combustion states during biomass burning. We show that the BC Eabs produced by flaming combustion may be up to two times more than those produced by smouldering combustion, suggesting that the particle morphology and mixing state of freshly emitted BC aerosols is an important source of the contrasting values of Eabs. The particle morphology of freshly emitted BC aerosols is widely assumed to be bare in models, which is rare in the ambient environment and leads to small estimates of Eabs by field observations. We conclude that the exact description of freshly emitted carbonaceous aerosols plays an important role in constraining aerosol radiative forcing.

The incidence of asthma exacerbation depends on atmospheric conditions, including such meteorological factors as the ambient temperature, relative air humidity or concentration of atmospheric aerosols. An assessment of relations between the frequency of asthma exacerbation and environmental conditions was made according to the meteorological components, the biometeorological index UTCI (Universal Thermal Climate Index), as well as selected air quality parameters, including concentrations of PM10 and PM2.5. The study was conducted on the basis of a retrospective analysis of medical data collected at the Independent Public Hospital of Tuberculosis and Pulmonary Diseases in Olsztyn (Poland). Our analysis of patient data (from 1 January 2013 until 31 December 2017) showed a significant correlation between the number of asthma exacerbation and the UTCI value. More frequent asthma exacerbations are observed in patients aged over 65 years when air humidity increases. The UTCI values contained within class 5, describing thermoneutral conditions, correspond to an average frequency of asthma exacerbation. A decline in the UTCI value leads to a reduced number of asthma exacerbation, while a rise makes the cases of asthma exacerbations increase.

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections., Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections.

North Atlantic sea surface temperatures have large climate impacts affecting the weather of the Northern Hemisphere. In addition to a substantial warming over much of the North Atlantic, caused by increasing greenhouse gases over the 21st century, climate projections show a surprising region of considerable future cooling at midlatitudes, referred to as the North Atlantic warming hole. A similar pattern of surface temperature trends has been observed in recent decades, but it remains unclear whether this pattern is of anthropogenic origin or a simple manifestation of internal climate variability. Here, analyzing state-of-the-art climate models and observations, we show that the recent North Atlantic warming hole is of anthropogenic origin. Our analysis reveals that the anthropogenic signal has only recently emerged from the internal climate variability, and can be attributed to greenhouse gas emissions. We further show that a declining northward oceanic heat flux in recent decades, which is linked to this surface temperature pattern, is also of anthropogenic origin., Most of the North Atlantic has seen strong increase in surface temperatures in recent decades, except for one region, which has been called the North Atlantic warming hole. Here, the authors employ detection and attribution techniques to show that this temperature pattern in recent decades is being caused by anthropogenic greenhouse gas emissions.

Slow-moving tropical cyclones (TCs) can cause heavy rain because of their duration of influence. Combined with expected increase in rain rates associated with TCs in a warmer climate, there is growing interest in TC translation speed in the past and future. Here we present that a slowdown trend of the translation speed is not simulated for the period 1951–2011 based on historical model simulations. We also find that the annual-mean translation speed could increase under global warming. Although previous studies show large uncertainties in the future projections of TC characteristics, our model simulations show that the average TC translation speed at higher latitudes becomes smaller in a warmer climate, but the relative frequency of TCs at higher latitudes increases. Since the translation speed is much larger in the extratropics, the increase in the relative frequency of TCs at higher latitudes compensates the reduction of the translation speed there, leading to a global mean increase in TC translation speed., How the translation speed of tropical cyclones has changed in recent decades and will change in the future has been the subject of debate. Model results show that on average, they have not slowed down in the past, but despite a slowing of tropical cyclones at higher latitudes, a poleward shift in their mean track location causes a general speed up under high greenhouse gas emissions.

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models. D.M.S., R.E., L.H., L.S.G., T.J., T.S., X.L., and P.O. were supported by the EU H2020 APPLICATE project (GA727862). The Met Office contribution was also supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. J.A.S was supported by NERC grants NE/P006760/1, NE/R005125/1 and NE/V005855/1. G.M and Y.P. were supported by the US Department of Energy, grant number DE-SC0019407. L.S.G was also supported by the Research council of Norway INES project (270061), and the Norwegian e-infrastructure for Research and Education (UNINETT Sigma2) through projects NN2345K, NS2345K and NS9034K. E.M. and D.M. acknowledge the support of the German Federal Ministry of Education and Research through the JPI Climate/JPI Oceans NextG-Climate Science-ROADMAP (FKZ: 01LP2002A) project and of the European Union’s Horizon 2020 Programme through the Blue-Action Project (GA727852); and the use of resources from the DKRZ bm0966 and bm1190 projects. C. Deser acknowledges support from the National Center for Atmospheric Research, which is a major facility sponsored by the US National Science Foundation under cooperative agreement 1852977. M.M. was supported by MEXT through the Integrated Research Program for Advancing Climate Models (JPMXD0717935457) and ArCS II (JPMXD1420318865) programs, and by the Environment Research and Technology Development Fund (JPMEERF20192004). J.G.-S. and P.O. were supported by the Spanish Ramón y Cajal’ programme (RYC-2016-21181, RYC-2016-22772). B.H. was jointly funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19070404) and the National Natural Science Foundation of China (Grant Nos. 42030602, 91837101). G.G. was supported by the EU H2020 Blue–Action (GA727852) project and uses the HPC resources of TGCC under the allocations 2018-R0040110492 and 2019-A0060107732 made by GENCI. J.S. acknowledges the project L4 of the Collaborative Research Centre TRR 181 Energy Transfers in Atmosphere and Ocean funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Project 274762653. Peer Reviewed "Article signat per 31 autors/es: D. M. Smith, R. Eade, M. B. Andrews, H. Ayres, A. Clark, S. Chripko, C. Deser, N. J. Dunstone, J. García-Serrano, G. Gastineau, L. S. Graff, S. C. Hardiman, B. He, L. Hermanson, T. Jung, J. Knight, X. Levine, G. Magnusdottir, E. Manzini, D. Matei, M. Mori, R. Msadek, P. Ortega, Y. Peings, A. A. Scaife, J. A. Screen, M. Seabrook, T. Semmler, M. Sigmond, J. Streffing, L. Sun & A. Walsh "

High-Arctic ecosystems are strongly affected by climate change, and it is still unclear whether they will become a carbon source or sink in the next few decades. In turn, such knowledge gaps on the drivers and the processes controlling CO2 fluxes and storage make future projections of the Arctic carbon budget a challenging goal. During summer 2019, we extensively measured CO2 fluxes at the soil–vegetation–atmosphere interface, together with basic meteoclimatic variables and ecological characteristics in the Bayelva river basin near Ny Ålesund, Spitzbergen, Svalbard (NO). By means of multi-regression models, we identified the main small-scale drivers of CO2 emission (Ecosystem Respiration, ER), and uptake (Gross Primary Production, GPP) in this tundra biome, showing that (i) at point scale, the temporal variability of fluxes is controlled by the classical drivers, i.e. air temperature and solar irradiance respectively for ER and GPP, (ii) at site scale, the heterogeneity of fractional vegetation cover, soil moisture and vegetation type acted as additional source of variability for both CO2 emissions and uptake. The assessment of the relative importance of such drivers in the multi-regression model contributes to a better understanding of the terrestrial carbon dioxide exchanges and of Critical Zone processes in the Arctic tundra.

The sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by $$82.26\%$$ 82.26 % , $$98.93\%$$ 98.93 % , and $$65.03\%$$ 65.03 % compared to its respective single model. The HS employing the LSTM improved $$92.15\%$$ 92.15 % , $$98.69\%$$ 98.69 % , and $$32.41\%$$ 32.41 % concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions.

The contributions of single greenhouse gas emitters to country-level climate change are generally not disentangled, despite their relevance for climate policy and litigation. Here, we quantify the contributions of the five largest emitters (China, US, EU-27, India, and Russia) to projected 2030 country-level warming and extreme hot years with respect to pre-industrial climate using an innovative suite of Earth System Model emulators. We find that under current pledges, their cumulated 1991-2030 emissions are expected to result in extreme hot years every second year by 2030 in twice as many countries (92%) as without their influence (46%). If all world nations shared the same fossil CO2 per capita emissions as projected for the US from 2016-2030, global warming in 2030 would be 0.4 °C higher than under actual current pledges, and 75% of all countries would exceed 2 °C of regional warming instead of 11%. Our results highlight the responsibility of individual emitters in driving regional climate change and provide additional angles for the climate policy discourse. ISSN:2662-4435