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Cirrus clouds play an important role in the Earth's radiative energy budget, thereby affecting the climate state and climate change. Orographic gravity wave (OGW)‐induced sub‐grid scale vertical velocity (i.e., cooling rate) is not resolved by large‐scale models and its impact on ice formation in cirrus clouds is not well quantified. In this study, one sub‐grid scale OGW scheme (e.g., McFarlane) is used in the Community Atmosphere Model version 6 (CAM6) to generate vertical velocity variance (σw) for cirrus formation. Results from the default model and simulations with the OGW‐induced σw are evaluated against the DOE ARM Small Particles in Cirrus (SPARTICUS) campaign observations. The OGW based on the McFarlane scheme increases the sub‐grid scale σw over mountains compared to the default model and improves the model agreement with the SPARTICUS observations. Larger σw due to OGWs can trigger more frequent homogeneous nucleation in orographic cirrus and generates a higher number concentration of ice crystals observed during the SPARTICUS campaign. Moreover, our evaluation of the model simulations against satellite observations indicates that the McFarlane scheme generates high in‐cloud ice number concentrations (>200 L−1) in the upper troposphere over mountains and high plateaus at mid‐ and high‐latitudes of the winter hemisphere as shown in the observations. More ice crystals with smaller sizes absorb more infrared radiation (+0.523 ± 0.125 W m−2). The net radiative cloud forcing change at the top of the atmosphere is +0.330 W m−2 due to the orographic cirrus. Plain Language Summary: Cirrus clouds occurring frequently in the upper troposphere play an important role in the Earth's radiative budget and the global climate. Large mountains in the globe can produce strong gravity waves propagating upward to the upper troposphere. These orographic gravity waves can induce large vertical velocities and form orographic cirrus which can extend hundreds of miles in the atmosphere. This study introduces orographic gravity waves induced by the mountains to cirrus ice formation in a global climate model, the Community Atmosphere Model version 6 (CAM6). We find that a large amount of ice crystals forms in the simulated cirrus over mountains in the winter hemisphere when orographic gravity waves are included in the cirrus formation. The simulation results are improved when comparing with aircraft and satellite observations. Orographic cirrus induces a warming effect on the global climate. Key Points: Vertical velocity variance (σw) generated by orographic gravity waves (OGWs) is introduced to the cirrus formation in CAM6The sub‐grid scale σw is increased by OGW‐induced fluctuations over mountains in orographic cirrus and agrees better with observationsThe large σw induced by OGWs generates high in‐cloud ice number concentrations (>200 L−1) in orographic cirrus as observed [ABSTRACT FROM AUTHOR]

In a warming climate, greenhouse gases modulate thermal cooling to space from the surface and atmosphere, which is a fundamental feedback process that affects climate sensitivity. Recent studies have found that when relative humidity (RH) is constant with global warming, Earth's clear‐sky longwave feedback would be dominated by surface cooling to space. Using a millennium‐length coupled general circulation model and accurate line‐by‐line radiative transfer calculations, here we show that the atmospheric cooling to space accounts for 12%–50% of the feedback parameter from poles to tropics. A simple yet comprehensive model is proposed here for explaining the atmospheric feedback process. It is found that when RH is held constant, the atmospheric feedback stabilizes the climate because (a) water vapor spectral lines are weakened by the collision‐broadening effect between water vapor and radiatively inert background gases, and (b) thermal emissions from other greenhouse gases increases due to enhanced Planck emission which is proportional to the surface warming. Each mechanism is responsible for half of the atmospheric feedback. We further elucidate that in hotter climates, the atmospheric feedback is more stabilizing because of (a) greater tropospheric opacity, and (b) more dramatic changes in air temperature with respect to transmission, owing to the pseudo‐adiabatic expansion of air with surface warming. The sum of surface and atmospheric feedback, the clear‐sky longwave feedback is accurately predicted by the simple model from the climate base state. Our study provides a theoretical way for assessing Earth's clear‐sky longwave feedback, with important implications for Earth‐like planets. Plain Language Summary: Observations and model simulations have shown that Earth maintains a stable longwave radiative feedback process. When the surface warms by 1 K, Earth allows for 1.7 to 2.0 Wm−2 of extra thermal cooling to escape to space in cloud‐free conditions. Recent studies have claimed that this enhanced thermal cooling to space can be explained by emissions from the surface passing through the atmosphere's infrared window. However, we find that a large portion of the stability actually results from enhanced atmospheric emission during global warming, which arises from the weakening of spectral lines broadening by radiatively inert gases (N2, O2, Ar) as the Earth warms. It is a well‐understood phenomenon in spectral physics but has been largely ignored in the feedback literature. As a result, the feedback responses from the thermal radiative effects of greenhouse gases tend to stabilize the climate, rather than initializing a runaway of thermal radiative energy. This study further proposes a simple theory for accurately predicting the clear‐sky longwave feedback from climate base states. Key Points: An atmospheric feedback process maintained by greenhouse gases crucially stabilizes Earth's climate under global warmingEarth's clear‐sky thermal energy budget is unlikely to runaway due to its stable atmospheric composition and thermodynamic structureA simple, analytical model can accurately predict the state‐dependent clear‐sky longwave feedback spectrum [ABSTRACT FROM AUTHOR]

The Arctic is rapidly warming, and tundra vegetation community composition is changing from small, prostrate shrubs to taller, erect shrubs in some locations. Across much of the Arctic, the sensitivity of shrub secondary growth, as measured by growth ring width, to climate has changed with increased warming, but it is not fully understood how shrub age contributes to shifts in climate sensitivity.We studied Siberian alder, Alnus viridis ssp. fruticosa, a large nitrogen‐fixing shrub that has responded to climate warming with northward range expansion over the last 50 years. We used serial sectioning of 26 individual shrubs and 94 cross‐sections to generate a 98‐year growth ring chronology, including one 142‐year‐old, Siberian alder in Northern Alaska. We tested how secondary growth sensitivity to climate has changed over the past century (1920–2017) and how shrub age affects climate sensitivity of alder growth through time.We found that over time, alder growth as expressed by the stand chronology became more sensitive to July mean monthly air temperature. Older shrubs displayed higher sensitivity to June and July temperature than younger alders. However, during the first 30 years of growth of any shrub, temperature sensitivity did not differ among individuals. In addition, the June temperature sensitivity of growth series from individual cross‐sections depended on the age of the attached shrub.Our results suggest that age contributes to climate sensitivity, likely through modifying internal shrub carbon budgets by changing size and reducing alder's dependence on N‐fixation over time. Older, more sensitive alder may enhance C and N‐cycling while having greater recruitment potential. Linking alder age to climate sensitivity, recruitment and total N‐inputs will enable us to better predict ecosystem carbon and nitrogen cycling in a warmer Arctic. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Uncertainty in the response of clouds to global warming remains a significant barrier to reducing uncertainty in climate sensitivity. A key question is the extent to which the dynamic component—that which is due to changes in circulation rather than changes in the thermodynamic properties of clouds—contributes to the total cloud feedback. Here, simulations with a range of cloud‐resolving models are analyzed to quantify the impact of circulation changes on tropical cloud feedbacks. The dynamic component of the cloud feedback is substantial for some models and is controlled both by sea surface temperature (SST) induced changes in circulation and nonlinearity in the climatological relationship between clouds and circulation. We find notable inter‐model differences in the extent to which ascending regions narrow or expand in response to a change in SST, which we link to differences in the longwave and shortwave dynamic components across models. The diversity of changes in ascent area is coupled to intermodel differences in non‐radiative diabatic heating in ascending regions. Plain Language Summary: Clouds influence Earth's energy balance by absorbing and reflecting solar and terrestrial radiation. The response of clouds to warming remains a key source of uncertainty in understanding how the climate system will evolve. In particular, how the influence of clouds on radiation is coupled to the atmospheric circulation is an open question. In this study, idealized simulations of the tropics at high spatial resolution (3 km) are analyzed to probe how changes in circulation impact clouds in a warming climate. It is found that, across a range of models, the degree to which circulation changes influence clouds depends on how the area of the region with ascending air responds to warming. Key Points: Influence of circulation changes on cloud feedbacks is substantial in some cloud‐resolving modelsComponent of cloud feedback associated with circulation changes is coupled to ascent areaIntermodel spread in response of ascent area linked to non‐radiative diabatic heating [ABSTRACT FROM AUTHOR]

We introduce a minimal stochastic lattice model for the column relative humidity (R) in the tropics, which incorporates convective moistening, horizontal transport and subsidence drying. The probability of convection occurring in a location increases with R, based on Tropical Rainfall Measuring Mission observations, providing a positive feedback that could lead to aggregation. We show that the simple model reproduces many aspects of full‐physics cloud resolving model experiments. Depending on model parameter settings and domain size and resolution choices, it can produce both random and aggregated equilibrium states. Clustering occurs more readily with larger domains and coarser resolutions, in agreement with full‐physics models. Using dimensional arguments and fits from empirical data, we derive a dimensionless parameter which we call the aggregation number, Nag, that predicts whether a specific model and experiment setup will result in an aggregated or random state. The parameter includes the moistening feedback strength, the horizontal moisture transport efficiency, the subsidence timescale, the domain size and spatial resolution. Using large ensembles of experiments, we show that the transition between random and aggregated states occurs at a critical value of Nag. We argue that Nag could help to understand the differences in aggregation states between full‐physics, cloud resolving models, which show little consensus about the robustness of self‐organized patterns, whose emergence appears to be sensitive to the model setup, physics and parameterizations. Plain Language Summary: Dynamical models of convection including detailed representation of microphysics and radiative processes can simulate idealized states of radiative‐convective equilibrium. Sometimes in these simulations the tropical atmosphere shows a behavior whereby all convective storms evolve from a state where they occur randomly in space, to one where they are clustered together, with large areas of the simulation domain free from convection. This phenomenon is important to understand due to its implications for climate sensitivity, but the problem is that these full‐physics models do not agree on when or how clustering occurs. We therefore introduce a much simpler stochastic model of the tropical atmosphere, whose minimal representation of the physics is nevertheless adequate to reproduce much of the behavior of the full‐physics models. Convection can aggregate or remain random, depending on the model parameter settings as well as the domain size and resolution chosen. The simplicity of the model allows us to derive a dimensionless parameter, which we call the aggregation number and incorporates all the model parameters and the domain size and resolution. Convection is found to aggregate when this parameter falls below a critical threshold. We suggest that this parameter can help to explain the differences between the full‐physics models. Key Points: A stochastic reaction‐diffusion model of the tropics can simulate aggregated and random convective statesThe model produces a similar sensitivity to domain size and resolution as full‐physics modelsA dimensionless parameter called the aggregation number combines model and experiment configuration parameters to predict aggregation onset [ABSTRACT FROM AUTHOR]

The climate response to the Mt. Pinatubo volcanic eruption is analyzed using large ensembles of Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations. In contrast to previous work, we find that standard measures of the global temperature response to volcanic forcing are not significantly correlated with climate sensitivity across models. Isolating the shortwave response due to non‐cloud effects does not improve the correlation with climate sensitivity. Earlier constraints on climate sensitivity based on the response to Mt. Pinatubo are consistent with having arisen by chance because of the small size of the ensembles used. Our results suggest that the response to Mt. Pinatubo cannot be used to constrain the climate sensitivity to increased greenhouse gas concentrations, as has been proposed, because the radiative feedbacks in response to volcanic eruptions are not well correlated with the feedbacks governing the long‐term response to greenhouse gas forcing. Plain Language Summary: Large volcanic eruptions influence climate by injecting sulfate aerosols into the stratosphere. It has been proposed that the response of the climate system to recent large eruptions can inform estimates of current and anticipated climate change driven by increasing levels of carbon dioxide in the atmosphere by comparing the simulated temperature responses driven by the two types of forcing across a variety of climate models. Here we show that numerous simulations are necessary to quantify the variability of the climate response to recent volcanic eruptions, and taken together the simulations show that the proposed relationship does not exist. Thus volcanic eruptions do not constrain 21st century climate change, and the results of earlier studies are consistent with having arisen by chance owing to the reduced number of simulations that were available. We propose that the lack of relationship is because climate feedbacks differ when driven by volcanic eruptions versus increased carbon dioxide. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) large ensembles allow the relationship between climate sensitivity and response to volcanic eruptions to be investigatedThe climate response to the eruption of Mt. Pinatubo does not correlate well with equilibrium climate sensitivity (ECS) across CMIP6 modelsPrevious claims that the response to Pinatubo could constrain ECS are consistent with statistical chance [ABSTRACT FROM AUTHOR]

The climate reconstruction of the Tibetan Plateau, which was called the Third Pole Environment, has been a research hotspot in recent years. However, there are few studies on C3 and C4 plants evolution and it is an area of active debate on whether there are C4 plants existing on the Tibetan Plateau. In this study, the paleoclimate of the Hurleg Lake on the northeastern Tibetan Plateau is reconstructed based on n‐alkane‐induced indicators, while the C4 plant evolution history is established by compound‐specific isotope of long‐chain n‐alkanes since 14.1 cal kyr BP. The response of C4 plant evolution history to climate change on the northeastern margin of the Tibetan Plateau demonstrated that C3 and C4 plants were sensitive to local and global climate change. Solar radiation in summer, insolation‐affected East Asian monsoon, and seasonal climate (including seasonal variation of the solar radiation, temperature, and precipitation) had significant effects on the long‐term growth of C4. On the other hand, factors such as temperature, precipitation, and other potential factors affected the detailed variation of C4 plants in a relatively short‐term. Our finding maintained the existence of C4 plants in the high‐altitude region and indicated that the abundance of C4 plants might increase significantly and the ecological pattern of vegetation on the Tibetan Plateau will change distinctly in the coming warmer climate conditions. Key Points: Paleoclimate and C4 plant evolution on the northeastern Tibetan Plateau since the Last Deglacial were revealedC4 plants on the northeastern Tibetan Plateau were relatively abundant during the Younger Dryas periodInsolation, temperature, East Asian monsoon, and seasonal differences play significant roles in the evolution of C4 plants [ABSTRACT FROM AUTHOR]

Using model simulations, we demonstrate that the climate response to localized tropical sea surface temperature (SST) perturbations exhibits numerous non‐linearities. Most pronounced is an asymmetry in the response to positive and negative SST perturbations. Additionally, we identify a "magnitude‐dependence" of the response on the size of the SST perturbation. We then explain how these non‐linearities arise as a robust consequence of convective quasi‐equilibrium and weak (but non‐zero) temperature gradients in the tropical free‐troposphere, which we encapsulate in a "circus tent" model of the tropical atmosphere. These results demonstrate that the climate response to SST perturbations is fundamentally non‐linear, and highlight potential deficiencies in work which has assumed linearity in the response. Plain Language Summary: Previous work has highlighted that Earth's energy balance is sensitive to the precise distribution of sea‐surface temperatures (SSTs), particularly in the tropical Pacific, with important implications for inferring climate sensitivity from the historical record. To quantify this "SST pattern effect," many studies have adopted a linear framework where the climate response to an SST anomaly is assumed to be a linear function of the sign and size of the anomaly. Here, we show that this assumption is not applicable in many parts of the tropics, particularly the Central Pacific. We classify these non‐linearities into two classes and explain why they arise in terms of simple tropical dynamics. To summarize our findings we use a conceptual model of the tropical atmosphere which we term the "circus tent" model, which represents the dynamics behind these non‐linearities in an intuitive way. Our work suggests that previous work which has assumed linearity in the climate response to tropical SSTs may be suffering from compensating biases in their response. Key Points: Contrary to assumptions made in previous work, we find that the climate response to tropical sea‐surface temperatures is highly non‐linearThese non‐linearities are most stark in the Central Pacific, and manifest even for very small perturbation magnitudesNon‐linearity is a fundamental consequence of tropical dynamics, with important implications for prior work using linear Green's functions [ABSTRACT FROM AUTHOR]

The physical and biogeochemical performance of 16 CMIP5 and 16 CMIP6 Earth System models (ESM) are examined relative to present day (1976–2005) observational data sets for a Ross Sea Region (RSR) containing the Ross Gyre (RG) and the Ross Sea Continental Shelf. A relative ranking scheme using error metrics and published ESM properties (including climate sensitivity parameters and anomalous deep ocean convection statistics) enables identification of a "best" ensemble of models for the RSR. Over the RSR the CMIP6 models are generally found to have improved physical representations compared to the CMIP5 set (based on our metrics for sea ice concentration, sea surface temperature, salinity, and height, and mixed layer depth), but the CMIP5 and CMIP6 biogeochemical representations remain similar. Examination of mean properties for the period 2081 to 2100 for RCP8.5 and SSP585 for CMIP5 and CMIP6, respectively, shows significant surface temperature increases, with significant decreases in sea surface salinity, sea ice concentration, and mixed layer depth across the RSR. Biogeochemically, there are generally small increases in surface values for chlorophyll, integrated primary production, and zooplankton carbon concentrations. The projections also have robust reductions in surface nitrate, phosphate, and silicate across the RSR. For that part of the RG circulation to the east of 180°E—which we refer to as the "inner RG"—significant barotropic transport increases are found by the end of century. Plain Language Summary: Global climate simulation models have been tested to see how well they represent observations of the ocean in a region around Antarctica known as the Ross Sea. Here we are interested in both the physical (temperature, flows etc) and biogeochemical (chlorophyll, nutrients etc) properties of the models compared to the observations. This process allows us to identify the "best" models, or at least to rank them all from "best" to "worst." This then provides us with more confidence about their future predictions, the argument being that models that do well for our present ocean will do better into the future. By the end of the century we find that the predictions show the surface ocean is warmer and fresher than today, with much reduced sea ice, and surface nutrients are depleted but the surface chlorophyll not so. The large scale circulation is also increased. This shows that the Ross Sea is likely to experience significant physical and biological changes over the coming century relative to today's world. Key Points: CMIP models ranked for their representation of the Ross Sea Region marine environment, and a "best" ensemble has been definedEnd of century projections show a warmer, fresher Ross Sea, and reduced sea ice concentration and mixed layer depths compared to present daySurface nutrients reduced by end of century, smaller changes to surface chlorophyll and Net Primary production, with increased Ross Gyre transport [ABSTRACT FROM AUTHOR]

Near‐equatorial zonal currents along 80.5°E in the upper Indian Ocean are key to tropical inter‐basin hydrographic exchanges. However, their meridional‐width variability and underlying causes have remained elusive. In this study, we use satellite‐derived ocean current data to reexamine the latitudinal structure of Wyrtki jets (WJs), Monsoon Circulation (MC), and South Equatorial Countercurrent (SECC) along 80.5°E on seasonal to interannual timescales. The WJs are characterized by a transient semiannual feature within the latitudinal range of 2.5°S–3°N, whereas the MC and the SECC have a dominant annual period within the latitudinal range of 3°–5.5°N and 2.5°–6°S, respectively. Empirical orthogonal function (EOF) analysis separates the seasonal latitudinal boundaries of the WJs, the MC, and the SECC, and reveals their interannual variability in response to anomalous wind forcing. EOFs1–3 represent the WJ, MC, and SECC modes accounting for 54%, 19%, and 14% of the total variance, respectively. The near‐equatorial zonal currents are asymmetrically responsive to the Indian Ocean Dipole (IOD), with an abnormally strong westward current resulting from the latitudinal superposition of the WJ and SECC modes in the autumn of 1997, 2006, and 2015 during strong positive IOD events. However, the abnormally enhanced eastward WJs occurred only during weak IOD events in the autumn of 1995, 1999, and 2005, accompanied by latitudinal destructive interference of strong westward SECC. Model sensitivity experiments show that the asymmetric response of near‐equatorial zonal currents to climate modes is closely related to the spatial differences in large‐scale wind anomalies associated with wind‐driven and eastern‐boundary‐generated waves. Plain Language Summary: This study examines and quantifies the meridional‐width variability of near‐equatorial zonal currents on seasonal to interannual timescales and diagnoses the associated forcing scenarios by using remote observations, ocean reanalysis, and a continuously stratified linear longwave ocean model. The frequency and phase decompositions reveal the seasonal latitudinal boundaries of the Wyrtki jets, Monsoon Circulation, and South Equatorial Countercurrent along 80.5°E. The interannual variability of the near‐equatorial zonal currents show an asymmetric response to the mature phases of strong positive and negative Indian Ocean Dipole events. The underlying mechanisms of these asymmetric processes are related to the spatial differences in the large‐scale wind anomalies and the associated wind‐driven and eastern‐boundary‐generated waves. Key Points: Frequency and phase decompositions of near‐equatorial zonal currents show the seasonal latitudinal boundaries of WJs, MC, and SECC along 80.5°EMeridional‐width variability of the near‐equatorial zonal currents shows an asymmetric response to strong pIOD and nIOD on interannual timescalesUnderlying mechanisms are related to spatial differences in large‐scale wind anomalies associated with wind/eastern‐boundary generated waves [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are challenged by difficulties in simulating cloud phase and cloud radiative effect over the Southern Ocean (SO). Some of the new‐generation GCMs predict too much liquid and too little ice in mixed‐phase clouds. This misrepresentation of cloud phase in GCMs results in weaker negative cloud feedback over the SO and a higher climate sensitivity. Based on a model comparison with observational data obtained during the Southern Ocean Cloud Radiation and Aerosol Transport Experimental Study, this study addresses a key uncertainty in the Community Earth System Model version 2 (CESM2) related to cloud phase, namely ice formation in pristine remote SO clouds. It is found that sea spray organic aerosols (SSOAs) are the most important type of ice nucleating particles (INPs) over the SO with concentrations 1 order of magnitude higher than those of dust INPs based on measurements and CESM2 simulations. Secondary ice production (SIP) which includes riming splintering, rain droplet shattering, and ice‐ice collisional fragmentation as implemented in CESM2 is the dominant ice production process in moderately cold clouds with cloud temperatures greater than −20°C. SIP enhances the in‐cloud ice number concentrations (Ni) by 1–3 orders of magnitude and predicts more mixed‐phase (with percentage occurrence increased from 15% to 21%), in better agreement with the observations. This study highlights the importance of accurately representing the cloud phase over the pristine remote SO by considering the ice nucleation of SSOA and SIP processes, which are currently missing in most GCM cloud microphysics parameterizations. Plain Language Summary: For decades, global climate models (GCMs) have exhibited large biases in simulating the radiative budget over the Southern Ocean (SO), mainly due to the poor simulation of SO clouds. Understanding the ice formation processes in SO clouds is critically important to simulating cloud effects on radiation as well as cloud feedback to global warming. In this study, we conduct an integrated model‐observational study to address a key area of uncertainty in GCMs, namely ice formation and evolution over the pristine SO. Model simulations of cloud and aerosol properties are compared against the observational data obtained during the Southern Ocean Cloud Radiation and Aerosol Transport Experimental Study. This study highlights the importance of accurately representing the cloud phase over the pristine remote SO by considering the ice nucleation of sea spray organic aerosols and secondary ice processes, which are currently missing in most GCM cloud microphysics parameterizations. Key Points: Important ice formation processes in Southern Ocean (SO) mixed‐phase clouds are missed by the Community Earth System ModelSea spray organic aerosols contribute a larger fraction of ice nucleating particles over the SO than dust aerosolsSecondary ice production processes are crucial for controlling ice crystal concentrations in moderately cold SO clouds [ABSTRACT FROM AUTHOR]

The magnitude of global surface temperature change in response to unit radiative forcing depends on the type and magnitude of forcing agent—a concept known as a "forcing efficacy." However, the mechanisms behind the forcing efficacy are still unclear. In this study, we perform a set of simulations using CESM1 to calculate the efficacy of 10 different forcing agents defined in terms of fixed‐SST effective radiative forcing, and then use a Green's function approach to show that each forcing efficacy can be largely understood in terms of the radiative feedbacks associated with the different surface temperature patterns induced by the forcing agents (a pattern effect). We also quantify how the state dependence of feedbacks on global mean surface temperature anomalies impacts forcing efficacies. The results show that the forcing efficacy can be well reconstructed with a combination of pattern effect and state dependence. Plain Language Summary: The magnitude of global warming in response to unit forcing induced by carbon dioxide is different to that induced by methane or solar radiation, and the efficacy of a specific climate forcing agent depends on its type and magnitude. Our results show that the forcing efficacy can be explained with a combination of pattern effect and state dependence. When there is relatively stronger forcing over the tropical western Pacific Ocean, where feedbacks are more negative, the corresponding sea surface warming pattern favors a lower efficacy. When the forcing induces a larger global surface warming, less‐stabilizing feedbacks are induced and the corresponding efficacy tends to be higher. Key Points: Forcing efficacy can be explained with a combination of pattern effect and state dependence of feedbacksEfficacy is lower when there is relatively stronger forcing over the tropical western Pacific OceanEfficacy is higher when the forcing is more positive, inducing less‐stabilizing feedbacks at higher warming [ABSTRACT FROM AUTHOR]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

The Pyrocumulonimbus (pyroCb) events over British Columbia in 2017 were observed in the lower stratosphere for about 8–10 months after the smoke injections. Several previous studies used global climate models to investigate the physical parameters for the 2017 pyroCb events, but the conclusions show strong model dependency. In this study, we use the Energy Exascale Earth System Model (E3SM) and complete an ensemble of runs exploring three injection parameters: smoke aerosol mass, the percentage of black carbon within the smoke aerosols, and plume injection height. Additionally, we consider the heterogeneous reaction of ozone and primary organic matter. According to the satellite daily observed aerosol optical depth (AOD), we find that the best ensemble member is the simulation with 0.4 Tg of smoke, 3% of which is black carbon, a 13.5 km smoke injection height, and a 10−5 probability factor of the heterogeneous reaction. Besides AOD, we examine the ensemble score based on the metrics used in previous studies: the metrics of the extinction coefficient at 18 km altitude and the maximum plume height. The conclusion of the best estimate of the injection parameters for 2017 pyroCb events shows strong not only model but also evaluation metric dependency. We use the Random Forest machine learning technique to quantify the relative importance of each parameter in accurately simulating the 2017 pyroCb events and find that the injection height is the most critical feature, no matter which metric is used to score the ensemble members. Plain Language Summary: Pyrocumulonimbus (pyroCb), or clouds caused by fires, can transport a large amount of smoke particles into the middle atmosphere, which then affects the climate. Several previous studies used global climate models, along with satellite observations of smoke, to investigate the key parameters for the 2017 pyroCb over British Columbia, but the conclusions show strong model dependency. This study uses the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) and completes an ensemble of simulations targeting three loosely constrained smoke injection parameters. The conclusion of the best estimate of the injection parameters for 2017 pyroCb events shows strong not only model but also evaluation metric dependency. We also use a machine learning technique to quantify the importance of each parameter in accurately simulating the 2017 pyroCb event. Finally, we find the E3SM model shows the most sensitivity to the smoke injection height. The estimated smoke lifetime for the 2017 pyroCb events is about 6 months, and a global‐averaged shortwave surface cooling of ∼‐0.3 W m−2 was observed for about 10 months. Key Points: An ensemble study of E3SM simulations determines the best combination of smoke parameters to match observations from the 2017 Pyrocumulonimbus (pyroCb) eventsThe random forest technique is used to quantify the relative importance of parameters and finds the greatest sensitivity to injection heightThe lifetime of stratospheric smoke from the 2017 pyroCb events is similar to the observations ranging from 137 to 188 days [ABSTRACT FROM AUTHOR]

Didymodon Hedw., with approximately 140 species in the family Pottiaceae, is distributed nearly throughout the world, with the greatest diversity and important ecological functions in drought lands and alpine ecosystems. Several studies involving morphology, molecular systematics, and macro‐systematic analysis have addressed the infrageneric classification of Didymodon, but controversy over the position of the infrageneric and species classification remains due to its high degree of morphological variation in micro‐habitats and strong sensitivity to climate change at regional and global scale. To date, only a few phylogenetic studies have been conducted with an incomplete number of Didymodon species; further, there is no study published regarding the divergence time of Didymodon. Consequently, we conducted a comprehensive phylogenetic analysis of Didymodon species, sampling a total of 107 species, based on one nuclear (ITS) and five chloroplast DNA. Moreover, divergence time analysis was conducted to infer the age of origin and divergence of Didymodon species. Our results presented the largest scale phylogenetic relationship of Didymodon to date and resolved the phylogenetic status of some controversial taxa and the new species. The divergence time estimation showed that Didymodon species originated around the early Cretaceous, and the diversification was concentrated in the Cretaceous and Eocene. Paleoclimate and environmental change have a direct impact on the origin and divergence of Didymodon species by shaping their morphology, resource availability and ecological niche. Our study will help understand species origin and speciation of Didymodon as well as reflecting species adaptability and experience to historical events. [ABSTRACT FROM AUTHOR]

Elevated atmospheric CO2 (eCO2) influences the carbon assimilation rate and stomatal conductance of plants, thereby affecting the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO2 in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO2 effects in observational data of real‐world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process‐based land surface model QUINCY to simulate the leaf‐level effects of increasing atmospheric CO2 concentrations and their century‐long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal‐to‐noise ratio to detect the emergence of the eCO2 effects. The eCO2 effect on gross primary productivity (GPP) emerges at relatively low CO2 increase (∆[CO2] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO2 effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (∆[CO2] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal‐to‐noise ratio. In general, the response to eCO2 is detectable earlier for variables related to the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest‐dominated rather than in grass‐dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO2 effects in in‐situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics. [ABSTRACT FROM AUTHOR]

Climate models are becoming increasingly sophisticated as climate scientists continually work to improve the realism with which the processes influencing Earth's climate are represented. One example is the treatment of cloud microphysics: as complexity is added to cloud microphysical schemes, Earth's energy budget can respond to changes in climate forcings, such as carbon dioxide or aerosols, in new ways. This increase in degrees of freedom has illuminated larger spread in climate sensitivity across the latest generation of climate models participating Coupled Model Intercomparison Project, Phase 6, with more high climate sensitivity models (Zelinka et al., 2020, https://doi.org/10.1029/2019gl085782). Whilst the historical record gives us just over a century of data to apply toward climate sensitivity constraints (e.g., Nijsse et al., 2020, https://doi.org/10.5194/esd-11-737-2020), the ocean is still taking up much of the heat trapped by anthropogenic greenhouse gas emissions and the climate system is far from equilibrium which limits our understanding how climate sensitivity might change in response to long‐term forced climate change. Here we discuss the valuable tests that paleoclimate reconstructions can provide the latest generation of climate models, as demonstrated by the recent study of Zhu et al., 2022, https://doi.org/10.1029/2021ms002776. Their study provides an example of the benefits for climate model development when climate models are confronted with simulating climates very different from today. Ideally the climate model development stage under future iterations of CMIP will involve such tests as an effort to constrain global climate sensitivity and the regional patterns of climate, such as polar amplification and subtropical aridification. Plain Language Summary: In each successive generation of climate models the representation of climate processes becomes more realistic and more complex. The more sophisticated characterization of cloud processes has resulted in some models warming much more in response to increasing atmospheric carbon dioxide concentrations due to stronger cloud feedbacks; referred to as having a higher climate sensitivity. The Community Earth System Model version 2 (CESM2) is one such model. When used to simulate the Last Glacial Maximum (LGM), CESM2 has a global temperature around 5°C colder than surface temperature reconstructions and previous generations of this model. By investigating the updated cloud scheme, Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 were able to identify and modify issues with the cloud microphysical scheme that were contributing to this problem. They subsequently produced a new version of CESM2 which can simulate the LGM more accurately without degrading the simulation of the 20th century climate. The Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 study is an example of how a paleoclimate can be used to identify issues with cloud schemes not traditionally recognized in the model development and validation process. Incorporating paleoclimate simulations earlier in the model development and validation process may help constrain cloud feedbacks and subsequently climate sensitivity. Key Points: Paleoclimates provide valuable tests for the latest generation of climate modelsZhu et al., 2022, https://doi.org/10.1029/2021ms002776 provide an example of the paleoclimate benefits for climate model developmentFuture iterations of CMIP will ideally incorporate paleoclimate tests during model development [ABSTRACT FROM AUTHOR]

Cloud‐phase partitioning has been studied in the context of cloud feedback and climate sensitivity; however, precipitation‐phase partitioning also has a significant role in controlling the energy budget and sea ice extent. Although some global models have introduced a more sophisticated precipitation parameterization to reproduce realistic cloud and precipitation processes, the effects on the process representation of mixed‐ and ice‐phase precipitation are poorly understood. Here, we evaluate how different precipitation modeling (i.e., diagnostic [DIAG] vs. prognostic [PROG] schemes) affects the simulated precipitation phase and occurrence frequency. Two versions of MIROC6 were used with the satellite simulator COSP2. Although the PROG scheme significantly improves the simulated cloud amount and snowfall rates, the phase partitioning, frequency, and intensity of precipitation with the PROG scheme are still biased, and are even worse than with the DIAG scheme. We found a "too frequent and too light" Arctic snowfall bias in the PROG, which cannot be eliminated by model tuning. The cloud‐phase partitioning is also affected by the different approaches used to consider precipitation. The ratio of supercooled liquid water is underrepresented by switching from the DIAG to PROG scheme, because some snowflakes are regarded to be cloud ice. Given that the PROG precipitation retains more snow in the atmosphere, the underestimation becomes apparent when other models incorporate the PROG scheme. This depends on how much precipitation is within the clouds in the model. Our findings emphasize the importance of correctly reproducing the phase partitioning of cloud and precipitation, which ultimately affects the simulated climate sensitivity. Plain Language Summary: This study examined cloud and precipitation phase partitioning (i.e., the ratio between liquid and ice) in the Arctic using the MIROC6 global climate model (GCM). Despite recent advances in precipitation modeling by GCMs, the associations between the macrostructures (i.e., cloud coverage and precipitation rate) and phase partitioning have been little studied. Prognostic treatment of precipitation, which is a more sophisticated parameterization, yields seasonal and annual cloud cover and snowfall that are in better agreement with satellite observations. However, it tends to generate snowfall too frequently and too lightly, resulting in the misrepresentation of precipitation phase partitioning. In addition, there is a risk of overestimating the ratio of cloud ice to cloud liquid by including prognostic precipitation. The bias is difficult to remove by model tuning alone. If the models misrepresent the precipitation phase partitioning, then the bias will further influence feedback processes in a future warming scenario through the snow‐to‐rain phase change, similar to the cloud phase feedback. Our findings emphasize the importance of conducting process‐oriented model evaluations on a regional scale. Key Points: We examined seasonal model biases in precipitation types of Arctic clouds using a satellite simulatorThere are compensating errors between the cloud amount and snowfall, even for the prognostic precipitation schemeRealistic cloud and precipitation processes and their phase partitioning cannot be achieved by model tuning [ABSTRACT FROM AUTHOR]

The McMurdo Dry Valleys (MDVs), Antarctica, represent a cold, desert ecosystem poised on the threshold of melting and freezing water. The MDVs have experienced dramatic signs of climatic change, most notably a warm austral summer in 2001–2002 that caused widespread flooding, partial ice cover loss and lake level rise. To understand the impact of these climatic disturbances on lake microbial communities, we simulated lake level rise and ice‐cover loss by transplanting dialysis‐bagged communities from selected depths to other locations in the water column or to an open water perimeter moat. Bacteria and eukaryote communities residing in the surface waters (5 m) exhibited shifts in community composition when exposed to either disturbance, while microbial communities from below the surface were largely unaffected by the transplant. We also observed an accumulation of labile dissolved organic carbon in the transplanted surface communities. In addition, there were taxa‐specific sensitivities: cryptophytes and Actinobacteria were highly sensitive particularly to the moat transplant, while chlorophytes and several bacterial taxa increased in relative abundance or were unaffected. Our results reveal that future climate‐driven disturbances will likely undermine the stability and productivity of MDV lake phytoplankton and bacterial communities in the surface waters of this extreme environment. [ABSTRACT FROM AUTHOR]

Aim: Understanding and predicting the biological consequences of climate change requires considering the thermal sensitivity of organisms relative to environmental temperatures. One common approach involves 'thermal safety margins' (TSMs), which are generally estimated as the temperature differential between the highest temperature an organism can tolerate (critical thermal maximum, CTmax) and the mean or maximum environmental temperature it experiences. Yet, organisms face thermal stress and performance loss at body temperatures below their CTmax, and the steepness of that loss increases with the asymmetry of the thermal performance curve (TPC). Location: Global. Time period: 2015–2019. Major taxa studied: Ants, fish, insects, lizards and phytoplankton. Methods: We examine variability in TPC asymmetry and the implications for thermal stress for 384 populations from 289 species across taxa and for metrics including ant and lizard locomotion, fish growth, and insect and phytoplankton fitness. Results: We find that the thermal optimum (Topt, beyond which performance declines) is more labile than CTmax, inducing interspecific variation in asymmetry. Importantly, the degree of TPC asymmetry increases with Topt. Thus, even though populations with higher Topts in a hot environment might experience above‐optimal body temperatures less often than do populations with lower Topts, they nonetheless experience steeper declines in performance at high body temperatures. Estimates of the annual cumulative decline in performance for temperatures above Topt suggest that TPC asymmetry alters the onset, rate and severity of performance decrement at high body temperatures. Main conclusions: Species with the same TSMs can experience different thermal risk due to differences in TPC asymmetry. Metrics that incorporate additional aspects of TPC shape better capture the thermal risk of climate change than do TSMs. [ABSTRACT FROM AUTHOR]

Herbarium records provide a broad spatial and temporal range with which to investigate plant responses to environmental change. Research on plant phenology and its sensitivity to climate has advanced with the increasing availability of digitized herbarium specimens, but limitations of specimen‐derived data can undermine the inferences derived from such research. One issue that has received little attention is collection site uncertainty (i.e. error distance), a measure of confidence in the location from which a specimen was collected. We conducted comparative analyses of phenoclimatic models to determine whether spatial deviations of 2, 5, 15 or 25 km between recorded and simulated collection sites, as well as the error distance reported in digitized records, affect estimates of the phenological sensitivity of flowering time to annual temperature and precipitation in a widespread annual California wildflower. In this approach, we considered both spatial and interannual variation in climatic conditions. Simulated site displacements led to increasingly weak estimates of phenological sensitivity to temperature and precipitation anomalies with increasing distances. However, we found no significant effect of reported error distance magnitude on estimates of phenological sensitivity to climate normals or anomalies. These findings suggest that the spatial uncertainty of collection sites among specimens of widely collected plant species may not adversely affect estimates of phenological sensitivity to climate, even though real discrepancies and georeferencing inaccuracy can negatively impact such estimates. Collection site uncertainty merits further attention as a potential source of noise in herbarium data, especially for research on how plant traits respond to spatial and interannual climatic variation. [ABSTRACT FROM AUTHOR]

The Columbia River basin is a large transboundary basin located in the Pacific Northwest. The basin spans seven US states and one Canadian province, encompassing a diverse range of hydroclimates. Strong seasonality and complex topography are projected to give rise to spatially heterogeneous climate effects on unregulated streamflow. The basin's water resources are economically critical, and regulation across the domain is extensive. Many sensitivity studies have investigated climate impacts on the basin's naturalized hydrology; however, few have considered the large role of regulation. This study investigates where and when regulation affects projected changes in streamflow by comparing climate outcomes across 80‐member ensembles of unregulated and regulated streamflow projections at 75 sites across the basin. Unregulated streamflow projections are taken from an existing data set of climate projections derived from Coupled Model Intercomparison Project version 5 Global Climate Models. Regulated streamflow projections were modeled by the US Army Corps of Engineers and the US Bureau of Reclamation by using these unregulated flows as input to hydro‐regulation models that simulate operations based on current and historical water demands. Regulation dampens shifts in winter and summer streamflow volumes. Regulation generally attenuates changes in cool‐season high flow extremes but amplifies shifts in warm‐season and annual high flow extremes at historically snow‐dominant headwater reservoirs. Regulation reduces dry‐season low flow changes in headwater tributaries where regulation is large but elsewhere has little effect on changes in low flows. Results highlight the importance of accounting for water management in climate sensitivity analysis particularly in snow‐dominant basins. Key Points: Regulation dampens future winter and summer volume changes where the degree of upstream regulation is largeRegulation dampens cool‐season high flow extreme increases and amplifies warm‐season increases at snow‐dominant headwater basinsRegulation dampens low flow changes in tributaries with a large degree of upstream regulation but has little to no effect elsewhere [ABSTRACT FROM AUTHOR]

We investigate the dependence of radiative feedback on the pattern of sea‐surface temperature (SST) change in 14 Atmospheric General Circulation Models (AGCMs) forced with observed variations in SST and sea‐ice over the historical record from 1871 to near‐present. We find that over 1871–1980, the Earth warmed with feedbacks largely consistent and strongly correlated with long‐term climate sensitivity feedbacks (diagnosed from corresponding atmosphere‐ocean GCM abrupt‐4xCO2 simulations). Post 1980, however, the Earth warmed with unusual trends in tropical Pacific SSTs (enhanced warming in the west, cooling in the east) and cooling in the Southern Ocean that drove climate feedback to be uncorrelated with—and indicating much lower climate sensitivity than—that expected for long‐term CO2 increase. We show that these conclusions are not strongly dependent on the Atmospheric Model Intercomparison Project (AMIP) II SST data set used to force the AGCMs, though the magnitude of feedback post 1980 is generally smaller in nine AGCMs forced with alternative HadISST1 SST boundary conditions. We quantify a "pattern effect" (defined as the difference between historical and long‐term CO2 feedback) equal to 0.48 ± 0.47 [5%–95%] W m−2 K−1 for the time‐period 1871–2010 when the AGCMs are forced with HadISST1 SSTs, or 0.70 ± 0.47 [5%–95%] W m−2 K−1 when forced with AMIP II SSTs. Assessed changes in the Earth's historical energy budget agree with the AGCM feedback estimates. Furthermore satellite observations of changes in top‐of‐atmosphere radiative fluxes since 1985 suggest that the pattern effect was particularly strong over recent decades but may be waning post 2014. Key Points: Post 1980 the Earth warmed with feedbacks uncorrelated with—and indicating much lower equilibrium climate sensitivity than—that expected for long‐term CO2 increaseSatellite observations of changes in top‐of‐atmosphere radiative fluxes since 1985 are in agreement with the modelsThe pattern effect may be waning post 2014 [ABSTRACT FROM AUTHOR]

We investigated the resolution sensitivity of the Global‐to‐Regional Integrated forecast SysTem global nonhydrostatic model characterized by explicit dynamics–microphysics coupling using varying uniform resolutions (120, 60, 30, 15, and 5 km). The experiments followed the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) winter protocol, which covers a 40‐day integration. These simulations did not activate parameterized convection. One 120 km test with parameterized convection was performed as a coarse‐resolution reference. Other model configurations for different simulations were kept as consistent as possible. Our results showed that the model gradually improved its representation of the fine‐scale features as the resolution increased. The 5 km simulation was overall close to a 3.75 km simulation during the first 12 days of the DYAMOND winter. With respect to the mean climate, the 5 km simulation had a more realistic rainfall distribution than the lower resolution explicit convection simulations. Cloud water and the related physical fields (e.g., shortwave cloud radiative forcing) had a large resolution sensitivity. The tropical rainfall frequency–intensity spectra became more realistic in the 5 km explicit convection simulation, but the 120 km run with parameterized convection showed a more realistic mean climate. As the resolution increases, the mean bulk effect of finely resolved model convection gradually converges to that of parameterized convection. The mean climate of this storm‐resolving model has slightly higher rainfall biases than a parameterized convection coarse‐resolution model, highlighting the importance of balancing resolved‐ and under‐resolved model convection for developing a unified multiscale global model. Key Points: The resolution sensitivity of the Global‐to‐Regional Integrated forecast SysTem nonhydrostatic model with an explicit‐convection setup is assessed from O (100 km) to O (km) scalesThe model gradually improves its representation of fine‐scale fluid structures with increasing resolutionThe interaction between cumulus ensemble and large‐scale flow of a Global storm‐resolving model overall converges to that of a parameterized convection model [ABSTRACT FROM AUTHOR]

Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (NCCN), cloud depth, and cloud duration of more than 3,000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or NCCN decreases. Cloud depth affects RR the most while NCCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of NCCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed. Plain Language Summary: Liquid clouds significantly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to complex processes influencing rain rates. Here, we present analyses of key factors affecting rain rate peak intensity in liquid clouds observed during a field campaign in central Argentina. Weather balloon soundings are combined with vertically pointing radar, lidar, and radiometer observations to define cloud elements passing overhead in time and height space along with their properties. Statistical models are applied to assess the relative impacts on rain rate of the liquid amount in the cloud, the aerosol particle concentration ingested into the cloud, and the cloud depth and duration. Rain rate increases as cloud depth, liquid amount, and cloud duration increase, or as aerosol concentration decreases. Cloud depth affects rain rate the most while aerosol concentration impacts it the least with significant uncertainty. The impact of in‐cloud turbulence on rain rate depends on time and location. Turbulence in the bottom of the cloud around the time of peak rain rate suppresses rain rate, while turbulence in the top of the cloud has variable effects. Key Points: Multiple linear regression and random forest models are used to assess controls on continental warm cloud peak rain ratePeak rain rate increases with cloud depth, liquid water path, and cloud duration, while it decreases with CCN concentration but with high uncertaintyTurbulence variably affects rain rate depending on timing and location with cloud base turbulence at time of peak rain rate suppressing it [ABSTRACT FROM AUTHOR]

Using a large suite of Green's function perturbation experiments, we construct the emergent dynamical operator for surface temperature and extract the most excitable mode. The leading mode turns out to be the most dominant mode excited by the climate forcing of doubling CO2, alone capturing 56% of the total spatial variances of the surface temperature response. The pattern of the leading mode is partly organized by the atmospheric annular modes in both hemispheres, and further modulated by the feedbacks from ocean circulations. Though derived from a single model, the leading mode can capture the spatio‐temporal variabilities of the global mean surface temperature over the past century in both the CMIP6 model simulations and observations. Moreover, the leading mode is most efficiently excited by radiative forcings from the midlatitude bands centered around 45°N and 25°S, where most human activities take place, suggesting the potency of human influence on the climate change. Plain Language Summary: Confidence in the regional features of climate change projections is a prized commodity for climate adaptation and mitigation. This study strives to decipher the pattern of the forced climate response using a dynamical mode‐based framework such that certain confidence may be established for regional climate projections. The dynamical modes, also known as the neutral modes, are extracted through singular value decomposition of the linear response function of a coupled climate model using a large suite of Green's function perturbation experiments. The leading mode turns out to be the most dominant mode excited by anthropogenic climate forcing, capturing the global mean surface temperature evolution over the past century in both observations and multi‐model simulations, lending confidence to the regional climate change features captured by the leading mode. Coincidentally, the leading mode is most efficiently excited by radiative forcings from the midlatitude bands centered around 45°N and 25°S where human activities are most prominent. Key Points: Green's function experiments are used to extract the most excitable modes and optimal forcing of surface temperature variabilityThe leading mode can capture the spatial‐temporal structure of the surface temperature variability over the past centuryMidlatitude radiative forcings due prominently to the human activities are effective in inducing global‐scale surface temperature changes [ABSTRACT FROM AUTHOR]

The climate of the early Eocene was characterized by much higher temperatures and a smaller equator‐to‐pole surface temperature gradient than today. Comprehensive climate models have been reasonably successful in simulating that climate in the annual average. However, good simulations of the seasonal variations, and in particular much warmer Arctic winters over land, have proven more difficult. Further, while increased greenhouse gases seems necessary to achieve an Eocene climate, it is unclear whether there is a unique combination of factors that leads to agreement with all available proxies. Here we use a very flexible General Circulation Model to examine the sensitivity of the modeled climate to differences in CO2 concentration, land surface properties, ocean heat transport, and cloud extent and thickness. Even in the absence of ice or changes in cloudiness, increasing the CO2 concentration leads to a polar‐amplified surface temperature change because of increased water vapor levels combined with the lack of convection at high latitudes, with the nonlinear dependence of longwave radiation on temperature amplifying the increase in winter over land. Additional low clouds over Arctic land generally decrease summer temperatures and further increase winter temperatures (except at very high CO2 levels). An increase in the land surface heat capacity, plausible given large changes in vegetation, also decreases the Arctic land seasonality. Thus, different combinations of factors—high CO2 levels, changes in low‐level clouds, and an increase in land surface heat capacity—can lead to a simulation within the proxy uncertainty range of the majority of proxy data. Plain Language Summary: During the early Eocene, some 50 million years ago, the Earth was approximately 13°C warmer and the equator‐to‐pole surface temperature difference was much smaller than it is today. We now have proxy data on the surface temperature at different latitudes and the seasonality of the surface temperature (for land at high‐latitudes), the amount of carbon dioxide in the air, the nature of the vegetation, and the land configuration. However, much of this data is quite uncertain. Modern climate models have been used to estimate what the Eocene climate was like, but they are complicated to use, hard to understand, and in some ways are tuned to the present climate. Here we use a simpler, more flexible climate model to simulate the Eocene climate and examine how differences in the CO2 concentration, land surface properties, ocean heat transport, and cloud extent and thickness affect the simulated climate. We find that, while increased CO2 is a necessary condition to achieving an Eocene climate, different combinations of surface albedo, cloudiness, and surface heat capacity of land can lead to simulations that are within the proxy uncertainty range of the majority of proxy data, including the reduced seasonality of Arctic land temperatures. Key Points: We offer physically plausible mechanisms for the warm winter climates over land in the EoceneModel simulations verify the mechanisms, and suggest that no exotic additional physics is neededHigh CO2 levels are essential, but various combinations of other parameters satisfy proxy data [ABSTRACT FROM AUTHOR]

Recent evidence suggests that the relationships between climate and boreal tree growth are generally non‐stationary; however, it remains uncertain whether the relationships between climate and carbon (C) fluxes of boreal forests are stationary or have changed over recent decades. In this study, we used continuous eddy‐covariance and microclimate data over 21 years (1996–2016) from a 100‐year‐old trembling aspen stand in central Saskatchewan, Canada to assess the relationships between climate and ecosystem C and water fluxes. Over the study period, the most striking climatic event was a severe, 3‐year drought (2001–2003). Gross ecosystem production (GEP) showed larger interannual variability than ecosystem respiration (Re) over 1996–2016, but Re was the dominant component contributing to the interannual variation in net ecosystem production (NEP) during post‐drought years. The interannual variations in evapotranspiration (ET) and C fluxes were primarily driven by temperature and secondarily by water availability. Two‐factor linear models combining precipitation and temperature performed well in explaining the interannual variation in C and water fluxes (R2 >.5). The temperature sensitivities of all three C fluxes (NEP, GEP and Re) declined over the study period (p <.05), and, as a result, the phenological controls on annual NEP weakened. The decreasing temperature sensitivity of the C fluxes may reflect changes in forest structure, related to the over‐maturity of the aspen stand at 100 years of age, and exacerbated by high tree mortality following the severe 2001–2003 drought. These results may provide an early warning signal of driver shift or even an abrupt status shift of aspen forest dynamics. They may also imply a universal weakening in the relationship between temperature and GEP as forests become over‐mature, associated with the structural and compositional changes that accompany forest ageing. [ABSTRACT FROM AUTHOR]

Intermittent rivers are the focus of a burgeoning subdiscipline of freshwater science, driven by mounting recognition of their ubiquity, biodiversity, and climate sensitivity. However, our understanding of their ecology is hampered by a reliance on unstandardised diversity measures, an underrepresentation of certain stream types in metacommunity research and a reticence to acknowledge biotic interactions as a potentially fundamental driver of catchment‐scale patterns in assemblage composition.This study analysed both local‐ and catchment‐scale responses of invertebrates to flow intermittence and hydrological isolation in an understudied ecosystem type (low‐gradient, groundwater‐fed headwaters). We compared responses of conventional (taxonomic richness, Shannon diversity) and more robust (coverage‐adjusted Hill–Shannon) α‐diversity metrics to drying, and analysed changes in β‐diversity components as a function of hydrological and spatial distances between samples. We also inferred the influence of biotic interactions on metacommunity structure, using hierarchical species distribution modelling to capture species‐to‐species associations after accounting for hydrological variability and dispersal traits.The conventional and robust α‐diversity metrics exhibited broadly similar shifts, although richness overestimated the decline in α‐diversity with intermittence and the Shannon index failed to detect a significant interaction between flow duration and hydrological isolation. Decomposition of β‐diversity revealed a dominance of species turnover over nestedness along the flow gradient, a finding contrary to prevailing conceptual wisdom in intermittent river ecology and one we attribute to the climatic and geological context of our study.Even after accounting for differences in hydrological niche occupation, we discovered a significant propensity for species to co‐occur with others of similar conservation status, implying that competition/colonisation trade‐offs involving rare specialists and widespread generalists could underpin metacommunity assembly in some drying headwater networks.Our results suggest that enhancing aquatic habitat connectivity in headwaters harbouring intermittent‐stream specialists could be counterproductive, and conservation plans should instead look to safeguard isolated habitats serving as biotic refuges for rare species. [ABSTRACT FROM AUTHOR]

Warming experiments with a uniformly insolated, non‐rotating climate model with a slab ocean are conducted by increasing the solar irradiance. As the global mean surface temperature is varied across the range from 289 to 319K, the sea surface temperature (SST) contrast at first declines, then increases then declines again. Increasing SST contrast with global warming is associated with reduced climate sensitivity, while decreasing SST contrast is associated with enhanced climate sensitivity. The changing SST contrast and climate sensitivity are both related fundamentally to the effect of water vapor on clear‐sky radiative cooling. The clouds in the convective region are always more reflective than those in the subsiding region and so always act to reduce the SST contrast. At lower temperatures between 289 and 297 K the shortwave suppression of SST contrast increases faster than the longwave enhancement of SST contrast. At warmer temperatures between 297 and 309 K the longwave enhancement of SST contrast with warming is stronger than the shortwave suppression of SST contrast, so that the SST contrast increases. Above 309 K the greenhouse effect in the subsiding region begins to grow, the SST contrast declines and the climate sensitivity increases. The transitions at 297 and 309 K can be related to the increasing vapor pressure path with warming. The mass circulation rate between warm and cool regions consists of shallow and deep cells. Both cells increase in strength with SST contrast. The lower cell remains connected to the surface, while the upper cell rises to maintain a roughly constant temperature. Plain Language Summary: A global model of a non‐rotating Earth with an ocean that stores heat but does not transport it is run to energy balance with different values of globally uniform solar heating. Despite the global uniformity of the system, it develops regions of warm sea surface temperature where rain and rising motion occur, and cooler regions with downward, subsiding air motion where rainfall does not occur. These contrasts between rainy and dry regions look very similar to what is observed in the present‐day tropics. As the climate is changed from current tropical temperatures toward warmer temperatures, the warm regions warm faster, mostly because the rising regions contain more water vapor. In this range of global temperatures the climate of this simple model is much less sensitive to increased solar heating than outside this range. These changes in the response of surface warming to increased solar heating are shown to arise from well‐understood physical processes that are expected to operate in nature. Key Points: The sensitivity of global climate is reduced when the Sea surface temperature (SST) contrast increases with global mean temperatureThe reduction in sensitivity is related to weakening of the clear‐sky greenhouse effect by increasing SST contrastThe large‐scale circulation consists of shallow and deep cells that both strengthen as the climate warms and the SST contrast increases [ABSTRACT FROM AUTHOR]

End of century projections from Coupled Model Intercomparison Project (CMIP) models show a decrease in precipitation over subtropical oceans that often extends into surrounding land areas, but with substantial intermodel spread. Changes in precipitation are controlled by both thermodynamical and dynamical processes, though the importance of these processes for regional scales and for intermodel spread is not well understood. The contribution of dynamic and thermodynamic processes to the model spread in regional precipitation minus evaporation (P − E) is computed for 48 CMIP models. The intermodel spread is dominated essentially everywhere by the change of the dynamic term, including in most regions where thermodynamic changes drive the multi‐model mean response. The dominant role of dynamic changes is insensitive to zonal averaging which removes any influence of stationary wave changes, and is also evident in subtropical oceanic regions. Relatedly, intermodel spread in P − E is generally unrelated to climate sensitivity. Plain Language Summary: Earth's hydroclimate will change in response to increased greenhouse gas concentrations. However, the physical process(es) whereby climate change leads to these hydroclimate change is still uncertain, especially on regional scales. Furthermore, models disagree as to the magnitude of these hydroclimate changes, and the causes of this intermodel spread are also not well understood. We demonstrate that uncertainty in future changes is driven almost everywhere by changes in the large scale winds, while the precise amount of warming simulated by a given model is largely irrelevant. This highlights that reducing uncertainty in future hydroclimate changes requires primarily narrowing uncertainties in the circulation response. Key Points: Even in regions where thermodynamic changes drive the multi‐model mean hydrologic changes, dynamic changes drive uncertaintyDynamic changes more important for intermodel spread even after zonal averaging and even over subtropical oceansNarrowing climate sensitivity will not help constrain future hydroclimate changes in most regions [ABSTRACT FROM AUTHOR]

Investigation of spatiotemporal variation in glacier velocity is imperative to comprehend glacier mass and volume loss as a function of their sensitivity to climate change. The long‐term glacier velocity record for the Eastern Himalayan region is of utmost importance owing to its data scarcity and climate sensitivity. Here, we present a long‐term dataset spanning more than two decades (1994–2020) of glacier surface velocity for the entire Sikkim Himalaya by applying image correlation methods on the multi‐temporal Landsat images. Our results demonstrate an average glacier surface velocity decline from 15.7 ± 5.69 (1994/96) to 12.88 ± 2.09 m yr−1 (2018/2020), that is a decline of ~15% during the period of investigation. Trend analysis shows a decreasing trend in median velocity (32.2%) at a rate of 0.25 m yr−1. Despite the general decline in average glacier velocity, the rate of slowdown of individual glaciers is extremely heterogeneous (3.6–20 m yr−1). Our study shows that up to 32% of the observed heterogeneity in velocity variation can be explained by the variation in glacier size. The present study highlights that large glaciers with thick ice cover move faster compared to small glaciers (even those situated on steep slopes). The findings are significant and have direct implications for assessing future water availability scenarios and modelling glacio‐hydrology in the region. [ABSTRACT FROM AUTHOR]

Floodplains of large rivers are exploited for agricultural production, industrial and municipal development, and transportation infrastructure. Recently, increased frequency of costly floods has prompted consideration of whether offsetting benefits might accrue from management of floodplains for ecosystem services. We employed a simple inundation model for 800 km of the Lower Missouri River, USA, to evaluate spatial and temporal distributions of ecological floodplain inundation metrics and how those distributions might vary with levee removal and climatic change. The model evaluates inundation at 30 × 30 m resolution on a daily basis over 82 years of record. We quantified provisioning of waterfowl habitat and potential denitrification. Spatial variability is affected by ongoing geomorphic adjustments that affect floodplain connectivity. Statistical models indicate that available floodplain area and recent aggradation are predictive of most inundation metrics. Connectivity is sensitive to climate‐change scenarios that predict increased floodplain inundation during spring waterfowl migrations; the greatest sensitivity to future climate exists where channel‐floodplain geomorphology presently enhances floodplain connectivity. Evaluation of floodplain denitrification indicates that on average, the nonleveed part of the floodplain could denitrify 0.05%–1.7% of the mean annual nitrogen load of the river. Levee removal could increase this rate to only 3.6% of the nitrogen load. The capacity of floodplain connectivity to influence certain ecosystem services is highly variable in space along the Lower Missouri River and may be appreciably influenced by climate change. Hence, decisions to optimize management of large‐river floodplains are likely to be highly location dependent. Plain Language Summary: Increased flooding on large, highly engineered rivers has prompted reconsideration of how to increase resiliency to flooding while also increasing net socioeconomic benefits. These concerns have prompted managers to consider how ecosystem services—such as provision of wildlife habitat or nutrient processing—may contribute to bottom‐line decisions about levee system designs. On the Lower Missouri River, USA, ongoing erosion and deposition along the channel exert a strong control on flooding patterns. Recognition of where and when floodplain areas flood, and a full accounting of costs and benefits of flooding, can be a useful component of floodplain management, especially when flooding potential varies substantially along the river. Key Points: Resiliency of large‐river floodplains to floods can be highly spatially variable, dependent on geomorphic channel adjustmentsFloodplain habitat for waterfowl increases significantly in areas of decreased channel conveyance and increased floodplain areaDenitrification potential, on average, is only 3.6% of N flux, showing that nutrient mitigation strategies in the floodplain are limited [ABSTRACT FROM AUTHOR]

There is low confidence in projections of Antarctic sea ice area (SIA), due to deficiencies in climate model sea ice processes. Ensemble regression techniques can help to reduce this uncertainty. We investigate relationships between SIA climatology and 21st century change in the Coupled Model Intercomparison Project, phase 6 (CMIP6) multi‐model ensemble. In summer, under a strong forcing scenario, each model loses the majority of its sea ice. Therefore, models with greater historical SIA exhibit greater reductions, so the observed climatology of SIA strongly constrains projections. Ensemble spread in historical summer SIA is smaller than in CMIP phase 5 (CMIP5), and CMIP6 gives a more robust constraint on future SIA. In winter, by 2100 under a strong forcing scenario, 40% of SIA disappears on average, and ensemble spread in historical mean SIA explains approximately half the spread in projected change. A greater winter ice loss in CMIP6 than CMIP5 is explained by the higher climate sensitivities of some CMIP6 models. Plain Language Summary: Antarctic sea ice area is important for ecosystems, human activity, and for the dynamics of the atmosphere and ocean. However, major climate change assessments such as the Intergovernmental Panel on Climate Change Sixth Assessment Report have placed little confidence in projections of Antarctic sea ice area. This is because climate models struggle to simulate many aspects of sea ice area as observed by satellites. However, not all models should be treated equally. Models with relatively more sea ice area in the recent past project greater losses in the future, especially in summer. This suggests that models with historical sea ice area closest to what we have observed may be more reliable in their future simulations. We use this information in a simple statistical model to show that, firstly, the newest climate models largely show near‐total sea ice loss in summer by the end of the 21st century, which was not universally true for older climate models. Secondly, newer climate models lose more winter sea ice than their predecessors in the same time period, which is related to the known greater global warming response to given greenhouse gas concentrations in the newer models. Key Points: We compare Coupled Model Intercomparison Project (CMIP), phase 5 and the CMIP, phase 6 (CMIP6) projections of Antarctic sea ice loss, assessing drivers of uncertainty and the robustness of projectionsUnder strong forcing, CMIP6 models lose most Antarctic summer sea ice by 2100, so projections are constrained by historical climatologyIncreased winter sea ice loss in CMIP6 is related to increased equilibrium climate sensitivity [ABSTRACT FROM AUTHOR]

We sample 30 calibration parameters of the CNRM‐CM6‐1 atmospheric component to produce a perturbed parameter ensemble. An error score aggregating four annual‐averaged metrics is used here as an example to represent the model performance. We propose a method using statistical emulators of climatic fields and feedback parameters to produce model candidates spanning the maximal range of net feedback while minimizing the error score. Optimal candidates are found for a range of climate sensitivity chosen to span 3.8–10.1 K. The candidates show large errors in the extremes of the climate sensitivity range and none of them have better scores than the released version of CNRM‐CM6. However, 65% of the optimal candidates have errors within the inter‐model standard deviation of the seven models participating in the Cloud Feedback Model Intercomparison Project, spanning and ECS range of (4.14–7.22 K). These optimal calibrations will be used to assess feedback diversity in coupled experiments. Plain Language Summary: In climate simulations, an important source of uncertainty comes from the representation of subgrid‐scale processes and the unconstrained parameters that it introduces. This parametric uncertainty has a potential impact on the global mean surface temperature response to a doubling in atmospheric CO2 concentration, measured as the climate sensitivity. Model calibration is often based on expert judgment and produce a single reference version of the model, used for global projections. In this study, we test different calibrations of the CNRM‐CM6‐1 atmospheric component in order to explore the diversity of estimated climate sensitivities. These model versions form a set of simulations called perturbed parameter ensemble (PPE). We evaluate the performance of the PPE members by comparing them to observational data sets of different fields (temperature, precipitation, radiative fluxes) and propose an optimal subset of calibrations maximizing performance across a range of climate sensitivities. Some of these optimal candidates show comparable performance than the reference model version, though with different climate sensitivities. Key Points: Parameters of CNRM‐CM6‐1 are varied to produce an ensemble of perturbed atmospheric simulationsWe propose a method to optimize model performance conditional on a target value of climate feedbackModel variants with comparable score to the reference version span a large climate feedback range [ABSTRACT FROM AUTHOR]

We focus on how different global temperature increases represented in IPCC reports have shifted over time. While the first four assessment reports had a roughly equal focus on temperatures above and below 2°C, the more recent fifth and sixth assessment reports have a considerably stronger focus on warming below 2°C. This is concerning as warming above 2°C is more likely given current emissions trajectories and is more influential on climate risk assessments. Key Points: The focus of the IPCC has shifted to lower temperatures, beginning with assessment report 5Only 14 % of the temperature mentions in assessment report 6 are above 2°C, in contrast to our current trajectory in which temperatures above 2°C seem likely [ABSTRACT FROM AUTHOR]

Experiments with seven Coupled Model Intercomparison Project phase 6 models were used to assess the climate feedback parameter for net historical, historical greenhouse gas (GHG) and anthropogenic aerosol forcings. The net radiative feedback is found to be more amplifying (higher effective climate sensitivity) for aerosol than GHG forcing, and hence also less amplifying for net historical (GHG + aerosol) than GHG only. We demonstrate that this difference is consistent with their different latitudinal distributions. Historical aerosol forcing is most pronounced in northern extratropics, where the boundary layer is decoupled from the free troposphere, so the consequent temperature change is confined to low altitude and causes low‐level cloud changes. This is caused by change in stability, which also affects upper‐tropospheric clear‐sky emission, affecting both shortwave and longwave radiative feedbacks. This response is a feature of extratropical forcing generally, regardless of its sign or hemisphere. Plain Language Summary: Understanding how the Earth's surface temperatures change in accordance with the anomalous energy flow into the system due to changes in greenhouse gases (GHGs) or anthropogenic aerosols is vital for predicting future temperature change. New data have made it possible to better calculate how efficiently the planet responds to temperature change (so as to return to energy equilibrium) for historical aerosols and GHGs. We find that the Earth requires greater surface temperature changes under aerosol climate forcing than it does for GHGs in order to balance out incoming and outgoing energy into the Earth system. By comparing with experiments that prescribe energy changes only outside the tropics, we find that the lower efficiency of aerosols in damping the radiative imbalance is related to their being mainly located away from the equator, unlike GHGs which are generally well mixed throughout the globe. This forcing away from the equator is tied to the vertical distribution of temperature changes. This distribution affects how efficiently surface temperature change leads to balancing the incoming and outgoing energy into the Earth system. Surface temperature can thus change differently for the same global average forcings. Key Points: Effective climate sensitivity is larger (feedback more amplifying) for historical anthropogenic aerosol than greenhouse‐gas (GHG) forcing in Coupled Model Intercomparison Project phase 6The key difference is that GHG forcing is global and aerosols are mainly extratropical (and aerosol hemispheric contrast unimportant)Extratropical forcing causes a shallower temperature response than tropical forcing, hence more amplifying cloud and lapse‐rate feedbacks [ABSTRACT FROM AUTHOR]

The response of terrestrial ecosystems to climate perturbations typically persist longer than the timescale of the forcing, a phenomenon broadly referred to as legacy. Understanding the strength of legacy is critical for predicting ecosystem sensitivity to climate extremes and the extent that persistent changes in surface‐atmosphere exchange might feedback onto climate. The cause of ecosystem legacy has been associated with myriad factors such as changes in aboveground biomass, however, few studies have tested how changes in the depth distribution of fine roots in response to perturbation might alter an ecosystem's recovery time. We explore this question using an Earth System Model that includes a dynamic root module where vegetation can forage for water and nutrients by altering their root profiles. The global simulations presented here show that in response to water stress events most ecosystems deepen their root profiles. In semi‐arid ecosystems, this response expedites recovery (i.e., less legacy) relative to simulations without dynamics roots because access to deeper water pools after the initial event remains favorable. In wetter ecosystems, the development of deeper root profiles slows down the recovery timescale (i.e., more legacy) because the deeper root profile reduces access to nutrients and is thus unfavorable. The simulations show that while the inclusion of dynamic roots might only minimally affect global patterns of Gross Primary Productivity and Evapotranspiration, the shift in root profile alters the timescale of recovery. Studies interested in the sustained response of land surfaces fluxes to climate disturbances should consider models that include dynamic root capability. Plain Language Summary: When vegetation is stressed from climate events such as drought, they alter the depths where roots are developed. This allow plants to better access resources that have become limited—typically water. Most Earth System Models do not include this dynamic and instead use a fixed root profile. It is unclear how the absence of dynamic roots affects the skill at which these models capture the response and recovery to climate stress events. Here, we use a climate model with dynamic roots to see how the presence of foraging roots affects the way different ecosystems respond to drought. We show that in drier ecosystems, vegetation develop deeper roots after drought and this allows the vegetation to recover more quickly from drought. Wetter ecosystems also develop deeper roots in response to stress, however, this actually slows down their rate of recovery. Ecosystem response to drought is a critical component of climate models and we show that models that lack dynamic roots miss a key component of terrestrial ecosystem recovery from stressful climate events. Key Points: Drought and pluvial events lead to shifts in root profiles as vegetation forages for water or other limited resourcesEarth System Model (ESM) simulations that include a dynamic root profile were used to assess how root dynamics influence ecosystem legacyDynamic roots enable semi‐arid (wet) ecosystems to recover more quickly (slowly) from perturbation, an effect not included in most ESMs [ABSTRACT FROM AUTHOR]

The Community Earth System Model version 2 (CESM2) simulates a high equilibrium climate sensitivity (ECS > 5°C) and a Last Glacial Maximum (LGM) that is substantially colder than proxy temperatures. In this study, we examine the role of cloud parameterizations in simulating the LGM cooling in CESM2. Through substituting different versions of cloud schemes in the atmosphere model, we attribute the excessive LGM cooling to the new CESM2 schemes of cloud microphysics and ice nucleation. Further exploration suggests that removing an inappropriate limiter on cloud ice number (NoNimax) and decreasing the time‐step size (substepping) in cloud microphysics largely eliminate the excessive LGM cooling. NoNimax produces a more physically consistent treatment of mixed‐phase clouds, which leads to an increase in cloud ice content and a weaker shortwave cloud feedback over mid‐to‐high latitudes and the Southern Hemisphere subtropics. Microphysical substepping further weakens the shortwave cloud feedback. Based on NoNimax and microphysical substepping, we have developed a paleoclimate‐calibrated CESM2 (PaleoCalibr), which simulates well the observed twentieth century warming and spatial characteristics of key cloud and climate variables. PaleoCalibr has a lower ECS (∼4°C) and a 20% weaker aerosol‐cloud interaction than CESM2. PaleoCalibr represents a physically more consistent treatment of cloud microphysics than CESM2 and is a valuable tool in climate change studies, especially when a large climate forcing is involved. Our study highlights the unique value of paleoclimate constraints in informing the cloud parameterizations and ultimately the future climate projection. Plain Language Summary: The Community Earth System Model version 2 (CESM2) shows a much higher equilibrium climate sensitivity (ECS > 5°C) than its predecessor models (≤4°C), which, if true, implies a greater future warming than previously thought and a more severe challenge for climate adaptation and mitigation. It is critical to determine whether the high ECS is realistic and what causes its increase. In a previous study, we suggested that the high ECS is likely unrealistic because CESM2 simulates excessive cooling for an ice age climate—the Last Glacial Maximum (LGM; ∼21,000 years ago). In this study, we investigate which aspects of CESM2 are responsible for the extreme LGM cooling and the high ECS. We find that the simulated LGM climate is very sensitive to treatments of cloud microphysical processes, and that removing an inappropriate limiter on cloud ice number and using a smaller time‐step size in the microphysics largely eliminate the excessive LGM cooling. With these microphysical modifications, CESM2 simulates a much lower ECS (∼4°C) and matches present‐day observations well. Our study suggests that an ECS > 5°C is likely unrealistic and highlights the importance of using past climates to inform and validate the model development including the treatment of clouds. Key Points: Excessive Last Glacial Maximum (LGM) cooling and an ECS > 5°C in Community Earth System Model version 2 are attributed to cloud microphysical processes including ice nucleationA new configuration (PaleoCalibr) is developed that removes an inappropriate cloud‐ice‐number limiter and decreases microphysical timestepPaleoCalibr simulates realistic LGM and modern climates, a lower ECS (3.9°C), and a weaker shortwave cloud feedback [ABSTRACT FROM AUTHOR]

Tropical highland environments present substantial challenges for climate projections due to sparse observations, significant local heterogeneity and inconsistent performance of global climate models (GCMs). Moreover, these areas are often densely populated, with agriculture‐based livelihoods sensitive to transient climate extremes not always included in available climate projections. In this context, we present an analysis of observed and projected trends in temperature and precipitation extremes across agroecosystems (AESs) in the northwest Ethiopian Highlands, to provide more relevant information for adaptation. Limited observational networks are supplemented with a satellite‐station hybrid product, and trends are calculated locally and summarized at the adaptation‐relevant unit of the AES. Projections are then presented from GCM realizations with divergent climate projections, and results are interpreted in the context of agricultural climate sensitivities. Trends in temperature extremes (1981–2016) are typically consistent across sites and AES, but with different implications for agricultural activities in the other AES. Trends in temperature extremes from GCM projected data also generally have the same sign as the observed trends. For precipitation extremes, there is greater site‐to‐site variability. Summarized by AES, however, there is a clear tendency towards reduced precipitation, associated with decreases in wet extremes and a tendency towards temporally clustered wet and dry days. Over the retrospective analysis period, neither of the two analysed GCMs captures these trends. Future projections from both GCMs include significant wetting and an increase in precipitation extremes across AES. However, given the lack of agreement between GCMs and observations with respect to trends in recent decades, the reliability of these projections is questionable. The present study is consistent with the "East Africa Paradox" that observations show drying in summer season rainfall while GCMs project wetting. This has an expression in summertime Ethiopian rain that has not received significant attention in previous studies. [ABSTRACT FROM AUTHOR]

We explore the CO2 dependence of effective climate sensitivity (SG) with symmetric abrupt and transient CO2 forcing, spanning the range 1/8×, 1/4×, 1/2×, 2×, 4×, and 8×CO2, using two state‐of‐the‐art fully coupled atmosphere‐ocean‐sea‐ice‐land models. In both models, under abrupt CO2 forcing, we find an asymmetric response in surface temperature and SG. The surface global warming at 8×CO2 is more than one third larger than the corresponding cooling at 1/8×CO2, and SG is CO2 dependent, increasing non‐monotonically from 1/8×CO2 to 8×CO2. We find similar CO2 dependence in the transient runs, forced with −1%yr−1CO2 and +1%yr−1CO2 up to 1/8×CO2 and 8×CO2, respectively. The non‐logarithmic radiative forcing—not the changing feedbacks—primarily explains the dependence of SG on CO2, particularly at low CO2 levels. The changing feedbacks, however, explain SG's non‐monotonic behavior. Plain Language Summary: Equilibrium climate sensitivity is the global mean warming after doubling CO2 concentrations from those of the year 1850. Since CO2 levels will likely surpass a doubling, it is crucial to know whether the amount of warming per CO2 doubling (which we refer to as the effective climate sensitivity, SG) is constant with each CO2 doubling or whether it changes. Necessary conditions for constant SG are (a) the radiative forcing introduced to the climate system from each CO2 doubling is constant and (b) the net radiative feedback does not change with CO2 levels. Current literature shows that SG will increase in a warmer world because the radiative feedback will change. We here investigate SG in both warmer and colder worlds, and confirm that SG increases at higher CO2 concentrations. However, we show that changes in the radiative forcing with each CO2 doubling are mainly responsible for SG increase with CO2, not feedback changes. Key Points: The global surface temperature responds asymmetrically to increased and decreased CO2 levels, in both abrupt and transient casesEffective climate sensitivity is higher with warming (2×, 4×, 8×CO2) than with cooling (1/2×, 1/4×, 1/8×CO2), in two different coupled modelsThe non‐logarithmic nature of the CO2 forcing is primarily responsible for the asymmetry, not the radiative feedbacks [ABSTRACT FROM AUTHOR]

Tropical anvil clouds are an important player in Earth's climate and climate sensitivity, but simulations of anvil clouds are uncertain. Here we identify and investigate one source of uncertainty by demonstrating a marked increase of anvil cloud fraction with resolution in cloud‐resolving simulations of radiative‐convective equilibrium. This increase in cloud fraction can be traced back to the resolution dependence of horizontal mixing between clear and cloudy air. A mixing timescale is diagnosed for each simulation using the cloud fraction theory of Seeley, Jeevanjee, Langhans, and Romps (2019) (https://doi.org/10.1029/2018GL080747) and is found to scale linearly with grid spacing, as expected from a simple scaling law. Thus mixing becomes more efficient with increasing resolution, generating more evaporation in middle and lower tropospheric updrafts. This decreases their precipitation efficiency (PE), thereby increasing their overall mass flux, leading to greater detrainment at the anvil level and hence higher anvil cloud fraction. The decrease in PE also yields a marked increase in relative humidity with resolution. Plain Language Summary: High anvil‐shaped clouds occurring in tropical thunderstorms are an important player in the climate system, but our understanding and simulations of this phenomena is uncertain. Here we show that the areal coverage of such "anvil" clouds in idealized simulations of the tropics is highly dependent on how finely the tropical atmosphere is represented in the simulation. Finer resolutions yield more evaporation and less rainfall per cloud, so then more clouds are required to provide the rainfall which the atmosphere requires, leading to an increase of cloudiness with resolution. Key Points: Cloud‐resolving simulations of radiative‐convective equilibrium exhibit a marked increase of anvil cloud fraction with resolutionThis sensitivity is closely related to the resolution‐dependence of evaporation and precipitation efficiencyThe root of these sensitivities is the resolution‐dependence of mixing between clear and cloudy air [ABSTRACT FROM AUTHOR]

This article introduces the second generation CMCC Earth System Model (CMCC‐ESM2) that extends a number of marine and terrestrial biogeochemical processes with respect to its CMIP5 predecessor. In particular, land biogeochemistry was extended to a wider set of carbon pools and plant functional types, along with a prognostic representation of the nitrogen cycle. The marine ecosystem representation was reshaped toward an intermediate complexity of lower trophic level interactions, including an interactive benthic compartment and a new formulation of heterotrophic bacterial population. Details are provided on the model setup and implementation for the different experiments performed as contribution to the sixth phase of the Coupled Model Intercomparison Project. CMCC‐ESM2 shows an equilibrium climate sensitivity of 3.57°C and a transient climate response of 1.97°C which are close to the CMIP5 and CMIP6 multi‐model averages. The evaluation of the coupled climate‐carbon response in the historical period against available observational datasets show a consistent representation of both physical and biogeochemical quantities. However, the land carbon sink is found to be weaker than the current global carbon estimates and the simulated marine primary production is slightly below the satellite‐based average over recent decades. Future projections coherently show a prominent global warming over the northern hemisphere with intensified precipitations at high latitudes. The expected ranges of variability for oceanic pH and oxygen, as well as land carbon and nitrogen soil storage, compare favorably with those assessed from other CMIP6 models. Plain Language Summary: Earth System Models integrate our knowledge on the underlying physical and biogeochemical mechanisms that drive or influence the global climate and the biosphere over the land and in the ocean. These models are used to provide realistic estimates of climate variability and its response to perturbations in the chemical constituents of the atmosphere and modifications of the terrestrial surface. This work describes the science at the base of the second generation Earth System Model developed at the Euro‐Mediterranean Centre on Climate Change and the major results obtained from the simulation of historical (from the pre‐industrial period until present) and different future scenarios up to 2100 in the context of the sixth Coupled Model Intercomparison Project. The model provides a solid representation of the present‐day physical climate and biosphere dynamics in comparison to available observations and data reconstruction of the recent past. The projected global warming signal and carbon accumulation within terrestrial and oceanic systems under future climate scenarios are comparable to the findings of other models involved in the sixth intercomparison project. Key Points: This work introduces the second generation CMCC Earth System Model (CMCC‐ESM2) and its configuration for CMIP6Estimated climate sensitivity and carbon‐climate feedbacks are similar to average of CMIP5 and locate in the lower end of CMIP6Both climate and biogeochemical dynamics are assessed through the comparison with observations and previous literature findings [ABSTRACT FROM AUTHOR]

The persistent inter‐model spread in the response of global‐mean surface temperature to increased CO2 (known as the "Equilibrium Climate Sensitivity," or "ECS") is a crucial problem across model generations. This work examines the influence of the models' present‐day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and find that they simulate a more poleward, and thus more realistic, edge of the Hadley cell (HC) in the Southern Hemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to the absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. In addition, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either. [ABSTRACT FROM AUTHOR]

Equilibrium climate sensitivity (ECS) and its related feedbacks are important metrics that can be used to measure the global mean surface temperature change in future climate projections, and the cloud feedback (CF) is considered to be the main contributor to ECS uncertainties. However, the use of different CF methods significantly affects the CF amplitudes, and the sign of the CF may also change. Combining the radiative kernel approach with a simplified CF method, the differences in the ECS and associated feedbacks between two versions of the Flexible Global Ocean–Atmosphere–Land System model (i.e., FGOALS‐g2 and FGOALS‐g3) were analyzed. Results show that the ECS of FGOALS‐g3 is smaller than that of FGOALS‐g2 (2.8 vs. 3.3 K). The main feedback processes that contribute to the ECS change in FGOALS‐g3 are the weaker surface albedo feedback and stronger negative shortwave CF. The reduced surface albedo feedback in FGOALS‐g3 can be attributed mainly to the differences in the simulations of sea ice area, surface temperature, and the Atlantic Meridional Overturning Circulation, as well as their interactions, compared with FGOALS‐g2. The enhanced negative shortwave CF in FGOALS‐g3 is directly related to the strengthened feedback of cloud area fraction and liquid water path. Furthermore, these changes can be traced back to the different atmospheric moist processes, parameter tuning, ocean grid, and external forcings used in FGOALS‐g3, as these all affect the mean climate state of the model. Plain Language Summary: Equilibrium climate sensitivity (ECS) is a good proxy for projected warming in CO2 forcing experiments. However, there is some uncertainty regarding ECS‐related feedbacks related to the different methods used to calculate them. Three methods, of differing complexity, that can be used to calculate cloud feedback (CF) are compared here, and the simplest method was selected to analyze the change in ECS between two versions of the Flexible Global Ocean–Atmosphere–Land System model (i.e., FGOALS‐g2 and FGOALS‐g3). The main causes of the ECS difference between FGOALS‐g3 and FGOALS‐g2 are the surface albedo feedback and the shortwave CF. These are further related to the different moist processes, parameter tuning, and ocean grids used in the two models. Key Points: The decrease in equilibrium climate sensitivity in FGOALS‐g3 is directly related to its stronger negative shortwave cloud feedbackThe changes in surface albedo and cloud feedbacks are linked to the changed mean state and feedback in FGOALS‐g3The differences in the feedbacks can be traced back to the upgraded model processes in FGOALS‐g3 [ABSTRACT FROM AUTHOR]

The mooring observations of the Overturning in the Subpolar North Atlantic Program reveal a significant freshening of the Iceland Scotland overflow waters that did not involve the Nordic Seas, the source of the dense Deep North Atlantic Water (Devana et al., 2021, https://doi.org/10.1029/2021GL094396). Their study suggests that this freshening at depth in the Iceland Basin stems from the largest upper‐ocean freshening event in 120 years that rapidly communicated through entrainment with the Iceland Scotland Overflow Waters. This communication, which is very likely driven by strong wintertime heat losses, strongly adds to our thinking that the progression of this extreme freshening event is providing us with a natural tracer that is helping to identify and understand key processes that determine the strength and variability of the overturning circulation and its sensitivity to ongoing climate change. Continued monitoring of the overturning in the North Atlantic is therefore necessary. Plain Language Summary: The largest surface freshening event in 120 years observed in 2016 in the eastern Subpolar North Atlantic is now found to have rapidly communicated with the deep waters that sustain the Atlantic overturning circulation, a rich system of ocean currents that is key to the European and global climate. Key Points: Overturning in the Subpolar North Atlantic Program moorings reveal a rapid communication between the largest upper‐ocean freshening event and the Iceland Scotland Overflow WatersSalinity variations of the Nordic Seas bottom waters reveal no connection to this recent deep ocean freshening in the Iceland BasinThe extreme rate of freshening has provided us with a natural tracer that is helping us identify and understand key Atlantic Meridional Overturning Circulation processes [ABSTRACT FROM AUTHOR]

Future projection of temperature extremes in the "Earth's three poles" (the Arctic, Antarctica, and Third Pole‐Tibetan Plateau [TP]) is of importance to risk assessment and policymaking owing to the high sensitivity to climate change in these regions. In this study, future projections of four extreme temperature indices were constructed after the application of a bias correction method in models of Phase 6 of the Coupled Model Intercomparison Project (CMIP6). The reduced intensification of temperature extremes in the Earth's three poles if warming can be limited to 1.5°C instead of 2°C above the pre‐industrial level was examined. Results showed that all the extreme temperature indices show significant increasing trends under both the SSP2–4.5 and SSP5–8.5 scenarios over the Earth's three poles (SSP: Shared Socioeconomic Pathway). For the coldest night (TNn), warmest night (TNx), and warmest day (TXx), the greatest increase by the end of the 21st century under SSP5–8.5 occurs in the Arctic, followed by the TP and finally Antarctica. For the coldest day (TXn), the greatest increase occurs in the Arctic, followed by Antarctica and finally the TP. If global warming can be limited to 1.5°C rather than 2°C, the intensification of TNn, TNx, TXn, and TXx in the Arctic (Antarctica/TP) under SSP5–8.5 is projected to reduce by 66% (21.7%/44.26%), 50.31% (54.79%/60.52%), 71.58% (12.91%/65.81%), and 41.73% (81.3%/57.34%), respectively, and the results are similar for SSP2–4.5. Therefore, keeping a lower warming target is essential for reducing the risk of extreme events in the Earth's three poles. Plain Language Summary: Efforts to understand and project climate change over the Earth's three poles (the Arctic, Antarctica, and Third Pole‐Tibetan Plateau) under global warming scenarios are crucial for risk assessment and policymaking aimed at coping with future climate change. This study reports the future change of four extreme temperature indices over the Earth's three poles based on the observational data sets and outputs from models of Phase 6 of the Coupled Model Intercomparison Project with bias correction. We find that global warming will lead to substantial changes in extreme temperature indices over the Earth's three poles. All the four extreme temperature indices with bias correction show consistent increasing trends under both Shared Socioeconomic Pathway (SSP)2–4.5 and SSP5–8.5 over the Earth's three poles. Although the future changes in extreme temperature indices under a 1.5°C or 2°C warming world are not uniform in space, the risk of temperature extremes over the Earth's three poles is likely to decrease when global warming is limited to 1.5°C instead of 2°C under both SSP2–4.5 and SSP5–8.5. This means that a lower warming target is necessary for reducing the risks of extreme temperature over the three poles. Key Points: All the four extreme temperature indices show consistent increasing trends under both Shared Socioeconomic Pathway (SSP)2–4.5 and SSP5–8.5 in the Earth's three polesThe increase of four extreme temperature indices in the Earth's three poles with global mean temperature is linearA lower warming target is necessary for reducing the risks of extreme temperature over the three poles [ABSTRACT FROM AUTHOR]

Despite the widespread use of the radiative kernel technique for studying radiative feedbacks and radiative forcings, there has not been any systematic, observation‐based validation of the radiative kernel method. Here, we utilize observed and reanalyzed radiative fluxes and atmospheric profiles from the Atmospheric Radiation Measurement (ARM) program and ERA5 reanalysis to assess a set of observation‐based radiative kernels from CloudSat for six ARM sites. The CloudSat radiative kernels, convoluted with the ERA5 state variables, can almost perfectly reconstruct the monthly anomalies of shortwave (SW) and longwave (LW) radiative fluxes in ERA5 at the surface (SFC) and top‐of‐atmosphere (TOA) with correlations significantly being greater than 0.95. The biases of kernel‐estimated flux anomalies calculated using the ARM‐observed state variables can be more than twice as large when compared with the ARM‐observed surface flux anomalies and Clouds and Earth's Radiant Energy System observed anomalies at the TOA. Generally, clouds contribute to most (>60%) of the variance of flux anomalies at Southern Great Plain (SGP), Tropical Western Pacific (TWP), and Eastern North Atlantic (ENA), and surface albedo dominates (>69%) the variance of SW flux anomalies at North Slope of Alaska. The radiative kernels exhibit the lowest correlation (r∼[0.55,0.85]) when reconstructing SFC LW flux anomalies at SGP, TWP, and ENA, whose biases are related to the possibility that the kernels may not fully capture the characteristics associated with Madden‐Julian oscillation and El Niño‐Southern Oscillation at TWP and the presence of clouds at SGP and ENA. Key Points: Observation‐based CloudSat radiative kernels are evaluated with field observations for six Atmospheric Radiation Measurement sites with different climate characteristicsThe kernels are found to be valuable for reconstructing observed radiative flux anomalies at the surface and top‐of‐atmosphereThe CloudSat kernels are the least skilled in reconstructing the observed changes in longwave radiative flux at the surface [ABSTRACT FROM AUTHOR]

Water temperature is increasingly acknowledged as a key variable for the sustainable management of lotic environments. Thermal variability in rivers dictates in large part the ecosystem functions of these water bodies. River thermal sensitivity (TS), defined in this work as the value of the regression slope between water and air temperature (Tair) measurements, is often used to determine how river temperature regime is expected to vary as climate evolves. This study proposes a method to contextualize climate conditions during the river temperature‐monitoring period in order to define a common basis to compare TS values calculated for different stations distributed across various climatic regions in the province of Québec (Canada). Nine climate classes were defined based on Tair and precipitation. Annual climate conditions were classified according to their anomalies compared to a reference period (1981–2010). For the reference climate class ("Normal–Normal" conditions), results indicate contrasted summer TS values between Québec rivers and within rivers. Furthermore, observed summer TS variations between climate classes underline the need to account for climate conditions when studying spatial variations in river thermal sensitivity. Climate contextualization also allows us to study the potential role of previous conditions. Rivers were grouped according to their drainage basins characteristics to examine spatial variability in TS for the chosen reference climate conditions. River slope, station elevation and geographic location partly explained TS spatial variability. Useful proxies are needed to describe the relative contribution of groundwater to river temperature and to potentially improve the group classification results. [ABSTRACT FROM AUTHOR]

We investigate how ocean feedbacks and the diurnal cycle impact convective aggregation using a slab ocean coupled to a cloud resolving model. With a 20 m mixed layer ocean, aggregation occurs after 25 days. Thinner ocean layers slow the onset of clustering, with a 1 m ocean layer needing around 43 days. The delay is due to anomalous solar radiation in clear sky regions, causing a relative surface warming balanced by a matching cooling in cloudy areas. The resulting gradient in surface heat fluxes opposes low level convergence into convecting regions. On aggregation, the convective regions are surrounded by moist, clear sky regions with the hottest SSTs, toward which convection migrates, while a cold SST patch forms under dry suppressed regions due to enhanced latent heat fluxes and longwave radiation. The next experiment allows a diurnal cycle of 2.5°C in the domain mean SST. This causes convective rainfall to shift from a weak morning maximum to a sharper evening peak, reminiscent of undisturbed tropical observations. However, convection reverts to a weak early morning maximum once aggregation starts due to spatially heterogeneous radiative forcing. This implies thin mixed ocean layers are a necessary but non‐sufficient condition for an afternoon maximum of convection; limited spatial water vapor variability is also necessary. The imposition of the mean diurnal cycle has no statistically significant impact on the mean timing of clustering onset. Plain Language Summary: Convection can spontaneously form clusters even in an initially homogeneous environment and understanding the processes that encourage or prevent clustering is important for our assessment of climate sensitivity. Many studies of convective clustering are conducted in idealized convection resolving models simulating convective towers over a fixed‐temperature ocean, precluding any surface feedbacks. We insert a reactive slab ocean model with a new adaptive control method whereby we can hold the area‐mean temperature fixed or allow it to undergo a diurnal temperature oscillation, while preventing any temperature drift. We find that spatial feedbacks with the ocean provide a strong deterrent to convection clustering, keeping convection random for much longer. Allowing a surface diurnal cycle in the mean temperatures results in an afternoon maximum of convection if the ocean mixed layer is thin, but only while convection is random. The diurnal cycle has no significant impact on clustering onset time, and as clustering onset occurs, the radiative impact of the spatial heterogeneity of water vapor dominates, and convection reverts to an early morning maximum. This suggests that relatively homogeneous water vapor is required in addition to suppressed conditions with thin ocean mixed layers in order to get an afternoon maximum over tropical oceans. Key Points: Interactive SSTs acts to slow the onset of convective aggregation, via shortwave cloud forcing and latent heat fluxesSlower aggregation onset is also highly variable, as expected for a weakly forced, bi‐stable stochastic systemAllowing a diurnal cycle in the mean surface temperature has no significant impact on aggregation timing [ABSTRACT FROM AUTHOR]