Search

The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation. Plain Language Summary: Many properties of the atmosphere are affected by the temperature of the ocean surface. Knowing how strong these effects are would help us to better predict global warming. The response to a given surface warming depends on where the warming occurs. To account for this, researchers sometimes simulate the response to individual patches of warming and then assume the response to an arbitrary warming pattern can be summed together from these patch responses. This is sometimes called the atmospheric Green's function method, and it works well at recreating the atmospheric response to historical temperature changes. We are organizing a Green's Function Model Intercomparison Project (GFMIP), in which participants will apply the method consistently for many climate models. This paper presents the GFMIP protocol. In the course of developing this protocol, we found that the atmospheric response to warming is not proportional in all cases: the response to surface warming is not the opposite of the response to surface cooling; warming twice as much doesn't cause twice as much of a response; and making a patch of warming twice as large doesn't cause twice as large a response. GFMIP will help us figure out how to account for this nonlinearity. Key Points: The Green's Function Model Intercomparison Project (GFMIP) explores the atmospheric response to surface temperature patch perturbationsThis paper presents the GFMIP protocol, which was generated using insights from past studies and new sensitivity testsGreen's functions reconstruct the response to historical temperatures, but nonlinearities can affect responses to other warming patterns [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

An important component of the risks from climate change arises from outcomes that are very unlikely, but whose impacts if they were to occur would be extremely severe. Examples include levels of surface warming, or changes in the water cycle, that are at the extreme of plausible ranges, or crossing of a climate system "tipping point" such as ice sheet or ocean circulation instability. If such changes were to occur their impacts on infrastructure or ecosystems may exceed existing plans for adaptation. The traditional approach of ensemble climate change projections is not well suited to managing these High Impact‐Low Likelihood (HILL) risks, where the objective is to "prepare for the worst" rather than to "plan for what's likely." In this paper we draw together a number of ideas from recent literature, to classify four types of HILL climate outcome and to propose the development of a practical "toolkit" of physical climate information that can be used in future to inform HILL risk management. The toolkit consists of several elements that would need to be developed for each plausible HILL climate outcome, then deployed individually to develop targeted HILL risk management approaches for individual sectors. We argue that development of the HILL toolkit should be an important focus for physical climate research over the coming decade, and that the time is right for a focused assessment of HILL risks by the Intergovernmental Panel on Climate Change in its 7th Assessment Cycle. Plain Language Summary: To prepare for the risks that arise from climate change (and avoid them where possible), it is important to understand how climate is likely to change in future, and what the impacts are likely to be. Over many years, climate science has developed sophisticated climate projections to estimate these likely impacts, and these are widely used to plan how people and societies will need to adapt to climate change. However it is also important to understand possibilities that are unlikely, but would have even more severe impacts if they did occur—for example, global warming levels at the high end of plausible estimates, or crossing a "tipping point" for major changes in ice sheets or ocean currents. A different type of information is needed to plan for these risks. In this paper we propose a new set of climate information "tools" to respond to these high‐impact risks. The tools include plausible scenarios of extreme outcomes, and early warning systems to detect if they are on the horizon. Combining these new tools with existing climate projections will allow society to understand more fully the risks of climate change, and to plan for the likely effects while preparing for the worst. Key Points: Climate outcomes or events that have a high impact are a key component of climate risk, even if their likelihood is lowTraditional climate projections are of limited use to inform management of high impact‐low likelihood risksPhysical climate science needs an increased focus on storylines, early warning and monitoring to inform management of high impact risks [ABSTRACT FROM AUTHOR]

A reference or "no‐feedback" radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where Te‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the "Planck feedback," averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming. Plain Language Summary: Earth's climate is stable because a warmer planet loses more energy to space, at infrared wavelengths invisible to the naked eye. The rate of change of this energy loss as the planet warms provides an estimate how Earth's energy balance responds to warming, which is simple enough to write on a small piece of paper. When scientists investigate the warming predicted by climate models, they often start from a similar but not identical calculation of how Earth's energy balance responds to warming. This calculation, based on model output, is about 15% less stabilizing than the simple pencil‐and‐paper estimate. In this paper, we explore the causes of this 15% difference between the pencil‐and‐paper estimate and the calculations using climate models. We show that the difference is primarily caused by the lack of assumed warming in climate models high in Earth's atmosphere, where temperatures are not closely linked to surface warming. This lack of warming acts as a hidden destabilizing feedback in current analysis of climate models. Key Points: Earth's reference radiative response, or "Planck feedback," is ∼0.5 W m−2 K−1 less stabilizing than a Stefan‐Boltzmann estimateWe find this deviation is mostly due to the assumed lack of stratospheric warming in calculations of the Planck feedbackThe lack of stratospheric warming serves as an implicit positive feedback in analysis of climate model warming [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

The emergence of 100‐Kyr glacial cycles (The Mid‐Pleistocene Transition [MPT]) is attributed in part to slower global overturning circulation and iron stimulation of biological carbon drawdown in the Southern Ocean. We present foraminifera‐bound nitrogen isotope values and polar planktic foraminifera abundances from the Agulhas Plateau that show that increases in biogenic sediment accumulation coincide with northward migrations of the Subtropical Frontal Zone (STFZ) and elevated foraminifera‐bound nitrogen isotope values during MPT glacial episodes. The nitrogen isotope values of two planktic foraminifera species, Globigerina bulloides and Globorotalia inflata show remarkable coherence amongst the sea surface temperature gradient between the STFZ and SAZ, and polar foraminifera abundances, indicating a strong relationship between nitrogen isotope dynamics above the Agulhas Plateau and migrations of the STFZ. Northward migration of the STFZ may have been essential to prolonging glacial intervals by increasing deep ocean carbon storage via a northward shift of the South Westerly Winds and a reduction in upwelling, delivery of fresher surface waters into the upper limb of global overturning circulation, or inhibiting heat and salt delivery to the Atlantic as Agulhas Leakage. Plain Language Summary: The Mid‐Pleistocene Transition (MPT) is a unique period of time, during which the natural rhythm of Earth's climate shifted, the pace of glacial and interglacial cycles changed from 40,000 years to 100,000 years as ice sheets expanded, carbon accumulated in the deep ocean, and the planet cooled. The exact cause of these changes is unclear, but internal system feedbacks were likely at play. As climate changes today and the oceans increasingly take up carbon, the MPT provides a unique case study to investigate Earth's natural climate system and important carbon cycle feedbacks. In this paper we use the nitrogen isotopic composition of plankton shells and other biogeochemical records to show that leading up to the MPT the Southern Ocean underwent major physical changes and expanded as 100,000 years glacial cycles emerged. Our data indicates that the expansion of the Southern Ocean delivered cold, nutrient rich waters northward where they could mix with warm, salty Indian and Atlantic Ocean waters as hypothesized by other studies. Key Points: Foraminifera‐bound nitrogen isotope values from the Agulhas Plateau record latitudinal migrations of the Subtropical FrontElevated foraminifera‐bound nitrogen isotope values after 900 ka are consistent with Southern sourced nitrateSouthern Ocean contraction during a "super interglacial" brings more subtropical nitrate to the Agulhas Plateau [ABSTRACT FROM AUTHOR]

Designing effective spatial management strategies is challenging because marine ecosystems are highly dynamic and opaque, and extractive entities such as fishing fleets respond endogenously to ecosystem changes in ways that depend on ecological and policy context. We present a modelling framework, marlin, that can be used to efficiently simulate the bio‐economic dynamics of marine systems in support of both management and research. We demonstrate marlin's capabilities by focusing on two case studies on the conservation and food production impacts of marine protected areas (MPAs): a coastal coral reef and a pelagic tuna fishery. In the coastal coral reef example, we explore how heterogeneity in species distributions and fleet preferences can affect distributional outcomes of MPAs. In the pelagic case study, we show how our model can be used to assess the climate resilience of different MPA design strategies, as well as the climate sensitivity of different fishing fleets. This paper demonstrates how intermediate complexity simulation of coupled bio‐economic dynamics can help communities predict and potentially manage trade‐offs among conservation, fisheries yields and distributional outcomes of management policies affected by spatial bio‐economic dynamics. [ABSTRACT FROM AUTHOR]

Warming climate and precipitation changes induce notable shifts in climate zones. In this study, the latest generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the previous generation CMIP5 under high‐emission scenarios are used together with observations and applied to the Köppen‐Geiger climate classification. The aim is to shed light on how projected warming and changes in precipitation will influence future climate zones and their associated ecosystems while revealing differences between the two model generations. Compared to CMIP5 models, CMIP6 models exhibit slightly improved performance in replicating the observed Köppen‐Geiger map for the historical (1976–2005) period and similar inter‐model agreement for the future. The models show major changes in climate zones with a range of projections depending on which ensemble subset is used: 37.9%–48.1% of the global land area is projected to change climate zone by the end of the century, with the most pronounced changes expected over Europe (71.4%–88.6%) and North America (51.2%–65.8%). CMIP6 models project a higher rate of areal climate zone change (km2/year) throughout the 21st century, which is mainly driven by their greater global land warming rates. Using a likely equilibrium climate sensitivity subset of CMIP6 models that is consistent with the latest evidence constrains the climate zone shifts, and their projections better match the results of CMIP5 simulations. Although the high warming rates of some CMIP6 models are less credible, the risks associated with them are greater, and they heighten the need for urgent action to preserve terrestrial ecosystems. Plain Language Summary: Köppen‐Geiger climate classification is a tool to map regional climate zones based on a region's temperature and precipitation. In recent decades, increasing temperatures and changing precipitation trends have started to affect the distribution of those climate zones, and more extensive changes are expected throughout the 21st century. In this paper, we assess the observed and expected changes in the global distribution of Köppen‐Geiger climate zones using observations along with the latest and the previous generation of global climate model projections. We find that shifts in climate zones are more pronounced in the latest generation of models due to their questionable greater global warming rate projections. Depending on which subset of model projections is used, up to half of the Earth's land area faces the risk of shifting to a different climate zone by the end of the century, with the greatest changes expected in Europe and North America. The rate of change (affected area per year) is also projected to accelerate through the 21st century, suggesting that vulnerable species and agricultural practices might have less time to adapt to changes in climate zones than previously projected. Key Points: Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits slightly improved performance in simulating the globally observed historical Köppen‐Geiger climate zones compared to CMIP5By the end of the century, 38%–48% of the global land area is projected to be in a different climate zone than todayThe rate of climate zone change is projected to accelerate, with more pronounced rates in CMIP6 due to considerably higher warming rates [ABSTRACT FROM AUTHOR]

Agriculture plays an important role in Senegalese economy and annual early warning predictions of crop yields are highly relevant in the context of climate change. In this study, we used three main machine learning methods (support vector machine, random forest, neural network) and one multiple linear regression method, namely Least Absolute Shrinkage and Selection Operator (LASSO), to predict yields of the main food staple crops (peanut, maize, millet and sorghum) in 24 departments of Senegal. Three combination of predictors (climate data, vegetation data or a combination of both) are used to compare the respective contribution of statistical methods and inputs in the predictive skill. Our results showed that the combination of climate and vegetation with the machine learning methods gives the best performance. The best prediction skill is obtained for peanut yield likely due to its high sensitivity to interannual climate variability. Although more research is needed to integrate the results of this study into an operational framework, this paper provides evidence of the promising performance machine learning methods. The development and operationalization of such prediction and their integration into operational early warning systems could increase resilience of Senegal to climate change and contribute to food security. [ABSTRACT FROM AUTHOR]

Intermittent and ephemeral streams in dryland environments support diverse assemblages of aquatic and terrestrial life. Understanding when and where water flows provide insights into the availability of water, its response to external controlling factors, and potential sensitivity to climate change and a host of human activities. Knowledge regarding the timing of drying/wetting cycles can also be useful to map critical habitats for species and ecosystems that rely on these temporary water sources. However, identifying the locations and monitoring the timing of streamflow and channel sediment moisture remains a challenging endeavor. In this paper, we analyzed daily conductivity from 37 sensors distributed along 10 streams across an arid mountain front in Arizona (United States) to assess spatiotemporal patterns in flow permanence, defined as the timing and extent of water in streams. Conductivity sensors provide information on surface flow and sediment moisture, supporting a stream classification based on seasonal flow dynamics. Our results provide insight into flow responses to seasonal rainfall, highlighting stream reaches very reactive to rainfall versus those demonstrating more stable streamflow. The strength of stream responses to precipitation are explored in the context of surficial geology. In summary, conductivity data can be used to map potential stream habitat for water‐dependent species in both space and time, while also providing the basis upon which sensitivity to ongoing climate change can be evaluated. [ABSTRACT FROM AUTHOR]

The relative importance of radiative feedbacks and emissions scenarios in controlling surface warming patterns is challenging to quantify across model generations. We analyze three variants of the Community Earth System Model (CESM) with differing equilibrium climate sensitivities under identical CMIP5 historical and high‐emissions scenarios. CESM1, our base model, exhibits Arctic‐amplified warming with the least warming in the Southern Hemisphere middle latitudes. A variant of CESM1 with enhanced extratropical shortwave cloud feedbacks shows slightly increased late‐21st century warming at all latitudes. In the next‐generation model, CESM2, global‐mean warming is also slightly greater, but the warming is zonally redistributed in a pattern mirroring cloud and surface albedo feedbacks. However, if the nominally equivalent CMIP6 scenario is applied to CESM2, the redistributed warming pattern is preserved, but global‐mean warming is significantly greater. These results demonstrate how model structural differences and scenario differences combine to produce differences in climate projections across model generations. Plain Language Summary: Earth System Models are used to project climate change through the 21st century. Such projections are constrained by a range of assumptions about emissions of greenhouse gases as well as other climate‐relevant human activities like deforestation and emissions of aerosols. These assumptions are carefully evaluated and represented in an emissions scenario. In a coordinated set of experiments, a given emissions scenario is applied to a number of different Earth System Models to test differences in climate projections that arise from physical (or "structural") differences among the models. However, over time, models are updated and improved, and emissions scenarios are updated as well. This continuous updating makes it difficult to track whether a new set of climate projections is different from its predecessor due to model changes or due to scenario changes. In this paper, we disentangle these two factors, clarifying the role of each. For the Earth System Model that we use, the emissions scenario largely controls the amount of global‐mean warming projected for the 21st century, but the model or structural differences matter greatly for the regional patterns of warming. Key Points: Higher equilibrium climate sensitivity, from more positive cloud feedbacks, only slightly increases 21st century transient warmingCloud and surface albedo feedbacks redistribute warming from the middle and high northern latitudes to lower latitudesThese warming pattern differences result from feedback differences, not CMIP5 to CMIP6 forcing scenario differences [ABSTRACT FROM AUTHOR]

Groundwater response to climate variations is often pivotal to managing groundwater sustainably. However, this relationship is rarely explicitly examined because of the complexity of surface to subsurface processes and the diverse impacts of multiple drivers, such as groundwater pumping and land use changes. In this paper, we address this challenge by proposing methods to quantify the sensitivity of groundwater level and recharge to temporal climate variability across Australia. Using the HydroSight groundwater hydrograph toolbox we first identify 1,143 out of a total of 4,350 bores as climate‐driven, where historically, head was primarily driven by climate variations. Streamflow elasticity measures are then adapted to groundwater to quantify the long‐term head and recharge sensitivity. We find that the national median sensitivity of head and recharge to precipitation change are 42 and 0.43 mm mm−1, respectively (interquartiles: 20–77 and 0.30–0.55 mm mm−1); both of which are ∼8 times that of potential evapotranspiration. Nationally, the results are spatially correlated, suggestive of large‐scale effects. The responses of head and recharge appear to be primarily related to climate type and hydrogeology. The more arid the climate, the higher the head sensitivity but the lower the recharge sensitivity. Porous media generally show higher head sensitivity than fractured media due to smaller aquifer specific yield, and again contrarily for that of recharge. These findings contribute to understanding the long‐term impact of climate change on groundwater and thus provide valuable insights for sustainable groundwater management. Plain Language Summary: In this study, we assess the response of groundwater to meteorological variations by using long‐term groundwater level records across Australia. We first identify the sites where the groundwater level has been primarily impacted by climate variations alone. The changes in groundwater level and replenishment rate (also called "recharge") at the natural sites are simulated under a range of precipitation and evapotranspiration shifts and their relationships are statistically quantified. Results show that the national median groundwater level changes by 42 mm and the recharge changes by 0.43 mm per 1 mm change in precipitation. The response of groundwater level and recharge are found to be primarily governed by the inherent properties of the sites, such as climate type and hydrogeology. This study provides valuable insights into climate change impacts on groundwater availability and contributes to developing adaptive water management strategies. Key Points: Climate‐dominated groundwater bores throughout Australia are statistically identified (n = 1,143, 26%)Median head and recharge sensitivity to precipitation are 42 and 0.43 mm mm−1Head and recharge sensitivity are primarily controlled by climate type and hydrogeology [ABSTRACT FROM AUTHOR]

Characteristics of, and fundamental differences between, the radiative‐convective equilibrium (RCE) climate states following the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts. Plain Language Summary: Clouds, circulations and rainfall in the tropics play an important role in Earth's climate. However, global climate models differ in their representation of these features, contributing to uncertainty in future climate projections. One useful tool to better understand model differences and inform efforts to improve models is to analyze idealized configurations. We explore two different numerical representations of an idealized atmosphere relevant to tropical regions to determine the impact on the characteristics of clouds, rainfall and circulations as well as the tropical atmospheric response to warming. We show that our idealized models mirror differences in the low‐cloud structure in the deep tropics of more realistic models. This work also finds similarities between the two numerical representations, such as a decrease in the spatial extent of high altitude clouds with warming. As the surface is warmed, both versions also show increases in the clustering of clouds, the likelihood of extreme precipitation rates, and an estimate of how much warming would occur in response to a doubling of carbon dioxide. Key Points: A new radiative‐convective equilibrium (RCE) configuration is added to the official release of Community Earth System Model (CESM) to facilitate the use of Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) in the CESM hierarchyThe Community Atmosphere Model version 5 and Community Atmosphere Model version 6 (CAM6) RCEMIP climate states and variation across SST experiments are documented in the context of the CESM hierarchyDiffering parameterizations lead to a reduction of low‐level clouds in CAM6, consistent with differences in more realistic simulations [ABSTRACT FROM AUTHOR]

Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms. Plain Language Summary: Being able to correctly simulate the amount of ice and liquid in clouds is essential for accurate predictions of the cloud radiative forcing in the climatologically sensitive polar regions. A number of collisional processes between ice and liquid particles in clouds, known as secondary ice production, can significantly enhance the ice crystal number concentrations contained in them. This enhancement is often accompanied by a decrease in the cloud liquid water content, resulting in less opaque clouds to incoming solar radiation, which, in turn, can cause a cloud‐induced warming at the surface. Currently most global climate models are missing the description of the most important secondary ice production processes, which can lead to a biased radiative impact of clouds at the surface. To address this, we propose using a machine learning algorithm trained on high‐resolution model outputs to include the effect of ice multiplication in large‐scale climate models. The machine learning framework effectively captures the physical processes underlying secondary ice production in stratiform clouds using only a few inputs readily available in model frameworks. This approach has the potential to improve model predictions bringing them closer to the observed cloud phase partitioning. Key Points: A random‐forest parameterization for secondary ice production is developed using outputs from a 10‐km horizontal grid spacing simulationCloud phase partitioning agrees within a factor of 3, with radiative biases below 3 Wm−2 compared to the detailed microphysics simulationThe scheme can be adjusted to coarser resolutions typical of climate models without losing computational efficiency and numerical stability [ABSTRACT FROM AUTHOR]

Basic ecological theory suggests that a tradeoff between competitiveness and stress tolerance dictates species range limits at regional extents. However, empirical support for this key theory remains deficient because the necessary spatial and temporal coverage and scalability of field observations has rarely been achieved. We harnessed an extensive dendroecological network (> 22 000 tree‐ring samples from 816 forest inventory plots) to disentangle competition‐limited from climate‐limited growth in both overstory and understory trees. Growth synchrony among trees thereby served as an integral metric of climate sensitivity, an approach that we justify in supplementary analyses of growth responses to temperature, precipitation, and the standardized precipitation‐evapotranspiration index. Sampling plots were arranged along elevational climate and vegetation gradients throughout the Carpathian Mountains, ranging from mixed‐species lowland forests to coniferous forests at high elevations. With mixed‐effect modelling, we also identified non‐climatic factors (stand characteristics, species diversity, and disturbance history) that modulate spatial patterns in the growth rate and synchrony of European beech Fagus sylvatica and Norway spruce Picea abies. Beech exhibited reduced growth and increased climate sensitivity towards higher elevations but performed better when species diversity was higher. The growth of spruce increased towards its lower range boundary, but understory cohorts grew poorly under interspecific competition. Overall, climate sensitivity was lower in more productive stands with benign climatic conditions and in recently disturbed sites with reduced stand density. These contrasting performances at mid‐elevations where the two species overlap (900–1300 m a.s.l.) reflect their evolutionary history, which enables them to be competitive (beech) or cold‐stress tolerant (spruce). This history will affect interactions between the two species under climate warming and shape macroecological patterns in the Carpathian ecoregion and likely other parts of Europe. Our findings point to a growing advantage of competitively stronger species in montane and subalpine vegetation zones. [ABSTRACT FROM AUTHOR]

Lake sediment records archive the Quaternary environmental and climatic history in northern high latitudes. Because of their spatial distribution, age range, time resolution, age control, and high sensitivity to paleoenvironmental conditions, lake records contribute to evaluating regional to hemispheric‐scale climate change. Here, we compare the characteristics of lake sediments and their qualities for paleoenvironmental and paleoclimatic reconstructions with those of other archives in northern high latitudes, and we outline the current state of research by introducing a series of regional case studies on 31 lakes as part of this special issue. The compilation shows that the coring site selection and interpretation can strongly benefit from bathymetric and seismic or hydro‐acoustic surveys. New coring techniques have become available making it possible to expand the records in length and time. Analytical work on the cores has particularly benefited from improved non‐destructive logging and scanning techniques, from the development of new proxies, and from advancements in quantitative climate reconstructions. Progress was also made in further developments of dating techniques; however, the recognition of additional isochronal events, e.g., via tephrochronology or paleomagnetic event stratigraphy, is needed to derive a better understanding of leads and lags in climate signal propagation throughout the northern high latitudes. [ABSTRACT FROM AUTHOR]

The equilibrium climate sensitivity estimated from different sources is inconsistent due to its dependence on the surface warming pattern. Cloud feedbacks have been identified as the major contributor to this so‐called pattern effect. We find a large unforced pattern effect in CERES data, with cloud feedback estimated from two consecutive 125‐month periods (March 2000–July 2010 and August 2010–December 2020) changing from −0.45 ± 0.85 to +1.2 ± 0.78 W/m2/K. When comparing to models, 27% of consecutive 10‐year segments in CMIP6 control runs have differences similar to the observations. We also compare the spatial patterns in the CERES data to those in climate models and find they are similar, with the East Pacific playing a key role. This suggests that the impact of the unforced pattern effect can be significant and that models are capable of reproducing its global‐average magnitude. Plain Language Summary: Climate sensitivity, the amount of surface warming in response to increasing carbon dioxide, is often used as a metric to quantify the severity of climate change. The estimates of climate sensitivity from different sources, however, are not consistent. The main reason is that the response of clouds to surface temperature changes, called cloud feedback, depends on the spatial pattern of surface warming. We investigate the impact of this so‐called pattern effect on cloud feedback in observations and climate models. Here we find that the observed cloud feedbacks calculated from two consecutive decades have significant differences in both global mean value and spatial pattern, and the climate models produce similar results. This highlights the fact that differences of surface warming patterns can have large influences on cloud feedbacks, and climate models have the ability to reproduce what happened in the real world. Key Points: The difference in cloud feedback between two ∼10‐year periods inferred from CERES is 1.6 ± 1.1 W/m2/KThe distinct difference in cloud feedback verifies the existence of a large unforced pattern effectCMIP6 models produce unforced pattern effects of similar magnitude and spatial structure to those seen in the observations [ABSTRACT FROM AUTHOR]

CO2 absorbs and emits radiation, which allows it to act both as radiative forcing and feedback. Recent work has shown CO2's feedback effect becomes dominant in hothouse climates, giving rise to a non‐monotonic climate sensitivity around 310 K. However, CO2's feedback effect in colder climates is less clear. We use a line‐by‐line model to explore the CO2‐dependence of the longwave clear‐sky feedback and identify a dividing temperature. Above 290 K, feedback increases with CO2 concentration; below 290 K, feedback decreases with CO2 concentration. We explain this dependence in terms of spectral competition under CO2 increases. In hot climates, CO2's moderate feedback replaces near‐zero feedback from the H2O bands; in cold climates, CO2's moderate feedback replaces the large feedback from the surface. Given that global mean temperature is currently close to 290 K, our results suggest that feedback CO2‐dependence is weak at present but can be important in past and future climates. Plain Language Summary: CO2 traps heat, causing warming. But CO2 also emits heat to space, acting as radiative feedback. Recent work has shown CO2's feedback effect crucially helps to stabilize very hot climates, but how does it affect present‐day Earth? We show that in hot climates, more CO2 increases Earth's feedback, while in cold climates, more CO2 decreases it. To understand why, we explain that the surface is an effective emitter, CO2 is a moderate emitter, while H2O is a poor emitter. At high temperatures, adding CO2 to the atmosphere thus replaces feedback that would have otherwise come from H2O, increasing the overall feedback; at low temperatures, adding CO2 replaces feedback that would have otherwise come from the surface, decreasing the overall feedback. Currently, Earth's global‐mean temperature falls between these two temperature regimes, where CO2's effect on feedback is nearly zero. Our results explain why CO2's impact on feedback is small now but can be significant in past or future climates. Key Points: An increase in CO2 concentration strengthens Earth's feedback in hot climates, ∂λ/∂CO2 > 0, but weakens it in colder climates, ∂λ/∂CO2 < 0Whether feedback CO2‐dependence, ∂λ/∂CO2, is positive or negative primarily depends on the extent of the H2O windowFeedback CO2‐dependence and forcing temperature‐dependence, ∂λ/∂CO2 = −∂F2x/∂Ts, can be important for past or future climates [ABSTRACT FROM AUTHOR]

A realistic representation of top‐of‐the‐atmosphere (TOA) radiation response to surface warming is key for trusting climate model projections. We show that coupled models with freely evolving ocean‐atmosphere interactions systematically underestimate the observed global TOA radiation trend during 2001–2022 in 552 simulations. Locally, even if a simulation spontaneously reproduces observed surface temperature trends, TOA radiation trends are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale surface warming pattern and from errors in the atmospheric physics affecting short‐ and longwave radiation. Models with a better representation of the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity. Our bias metric is a novel process‐based approach which links a model's current response to climate change to its behavior in the future. Plain Language Summary: A realistic representation of the radiation balance at the top of the Earth's atmosphere (TOA) by coupled climate models is essential for trust in future climate projections. Despite that relevance, it is still not clear whether the models correctly simulate the coupling between surface warming and TOA radiation because of the short observational record. We show that climate models systematically underestimate the observed increase in global TOA radiation during 2001–2022 in 552 simulations. Locally, even if a simulation reproduces observed changes in surface temperature, changes in TOA radiation are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale patterns of surface warming and from errors in the atmospheric physics which suppress the communication of the surface information to the TOA. Models that better represent the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity, that is, a weak global‐mean surface warming in response to a doubling of the atmospheric CO2 concentration above pre‐industrial levels. Our new bias metric links a model's current response to climate change to its behavior in the future. Key Points: Climate models underestimate the observed global TOA radiation trend during 2001–2022Surface warming patterns and atmospheric physics matter for the observation‐model discrepancyModels with a small bias in the coupling between surface warming and TOA radiation trends have a relatively low climate sensitivity [ABSTRACT FROM AUTHOR]

This work explores the impact of rotation on tropical convection and climate. As our starting point, we use the RCEMIP experiments as control simulations and run additional simulations with rotation. Compared to radiative convective equilibrium (RCE) experiments, rotating RCE (RRCE) experiments have a more stable and humid atmosphere with higher precipitation rates. The intensity of the overturning circulation decreases, water vapor is cycled through the troposphere at a slower rate, the subsidence fraction decreases, and the climate sensitivity increases. Several of these changes can be attributed to an increased flux of latent and sensible heat that results from an increase of near‐surface wind speed with rotation shortly after model initialization. The increased climate sensitivity results from changes of both the longwave cloud radiative effect and the longwave clear‐sky radiative fluxes. This work demonstrates the sensitivity of atmospheric humidity and surface fluxes of moisture and temperature to rotation. Plain Language Summary: Many useful studies of the tropical regions of Earth have neglected to include rotation; however, phenomena such as tropical cyclones and atmospheric waves are fundamentally tied to rotation. This work compares models of the tropics with and without rotation. Compared to experiments without rotation, rotating experiments have a more humid and stable atmosphere. Although water vapor is cycled through the atmosphere at a slower rate, the precipitation rate increases. The climate sensitivity also increases due to changes in both clouds and water vapor. Many of these changes are the result of increased wind speeds. We argue that idealized modeling of Earth's tropical regions should include not just stationary experiments but also experiments that include rotation. Key Points: Rotation leads to a decreased intensity of the overturning circulation but increased mean precipitation and precipitable waterStatic stability increases with rotation, net radiative cooling remains approximately constant, resulting in a smaller subsidence velocityRotating experiments have a larger effective climate sensitivity relative to RCE experiments mostly due to longwave radiative changes [ABSTRACT FROM AUTHOR]

Radiation schemes are critical components of Earth system models that need to be both efficient and accurate. Despite the use of approximations such as 1D radiative transfer, radiation can account for a large share of the runtime of expensive climate simulations. Here we seek a new state‐of‐the‐art in speed and accuracy by combining code optimization with improved algorithms. To fully benefit from new spectrally reduced gas optics schemes, we restructure code to avoid short vectorized loops where possible by collapsing the spectral and vertical dimensions. Our main focus is the ecRad radiation scheme, where this requires batching of adjacent cloudy layers, trading some simplicity for improved vectorization and instruction‐level parallelism. When combined with common optimization techniques for serial code and porting widely used two‐stream kernels fully to single precision, we find that ecRad with the TripleClouds solver becomes 12 times faster than the operational radiation scheme in ECMWF's Integrated Forecast System (IFS) cycle 47r3, which uses a less accurate gas optics model (RRMTG) and a more noisy solver (McICA). After applying the spectral reduction and extensive optimizations to the more sophisticated SPARTACUS solver, we find that it's 2.5 times faster than IFS cy47r3 radiation, making cloud 3D radiative effects affordable to compute in large‐scale models. The code optimization itself gave a threefold speedup for both solvers. While SPARTACUS is still under development, preliminary experiments show slightly improved medium‐range forecasts of 2‐m temperature in the tropics, and in year‐long coupled atmosphere‐ocean simulations the 3D effects warm the surface substantially. Plain Language Summary: A crucial step in simulating weather and climate is calculating how atmospheric radiation (shortwave radiation from the sun and terrestrial longwave radiation) interacts with the Earth's atmosphere and surface. The complexity of the underlying physics has necessitated making approximations in how radiative transfer is treated, such as assuming that radiation can only enter or leave a cloud through its top or base, thereby ignoring 3D effects. Even so, radiative transfer has historically been one of the computationally most demanding steps in making weather and climate simulations. Here we show that a state‐of‐the‐art radiation code can be sped up threefold by using code optimization techniques that seek to maximize performance on modern processors. Combining this with a recent innovation that reduces the number of spectral computations required for accurate solutions, an order‐of‐magnitude increase in speed is obtained compared to the existing radiation scheme in a global weather model. Crucially, these improvements also make a radiation scheme that accounts for cloud 3D radiative effects fast enough to be used operationally. When included in global simulations, these 3D effects act to warm the lower atmosphere substantially. Key Points: Restructuring and serial code optimization is performed for the ECMWF radiation schemeWhen combined with spectrally reduced gas optics, a speed‐up of 12 is obtained compared to the operational schemeCloud 3D radiative effects become affordable to compute in dynamical models [ABSTRACT FROM AUTHOR]

The effective climate sensitivity in the Department of Energy's Energy Exascale Earth System Model (E3SM) has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly due to a weaker positive cloud feedback that leads to a stronger negative radiative feedback. Present‐day atmosphere‐only experiments with uniform 4 K sea surface temperature warming are used to separate the contributions of individual model modifications to the reduced cloud feedback. We find that the reduced cloud feedback is mostly driven by changes over the tropical marine low cloud regime, mainly related to a new trigger function for the deep convection scheme and modifications in the cloud microphysics scheme. The new trigger function helps weaken the low cloud reduction by increasing the cloud water detrainment at low levels from deep convection under warming. Changes to the formula of autoconversion rate from liquid to rain and an introduced minimum cloud droplet number concentration threshold in cloud microphysical calculations help sustain clouds against dissipation by suppressing precipitation generation with warming. In the midlatitudes, the increased Wegener‐Bergeron‐Findeisen (WBF) efficiency strongly reduces present‐day liquid water and leads to a stronger negative cloud optical depth feedback. The reduced trade cumulus cloud feedback in v2 is closer to estimates from recent observational and large‐eddy modeling studies but might not be due to the right physical reasons. The reduced mid‐latitude cloud feedback may be more plausible because more realistic present‐day mixed‐phase clouds are produced through the change in the WBF efficiency. Plain Language Summary: Understanding how the Earth responds to greenhouse gas increases is important for climate change research. In the Department of Energy's Energy Exascale Earth System Model, the global temperature response to an abrupt quadrupling of atmospheric carbon dioxide has decreased from 5.3 K in version 1 to 4.0 K in version 2. This reduction is mainly because low‐level clouds over the tropical oceans decrease less as the planet warms in version 2: a weaker amplifying cloud feedback. To understand the reasons behind this reduction, warming simulations were conducted to separate the contributions of individual model changes. We find that the reduced cloud feedback is primarily due to changes in the representation of the vertical movement of air through the depth of the lower atmosphere and of the microscopic properties of clouds. The findings highlight some unexpected impacts on cloud feedback resulting from modifications to the model's physics and emphasize the importance of monitoring and understanding changes in cloud feedback during model development. Key Points: E3SM's effective climate sensitivity is lower in version 2 mainly due to the reduced positive cloud feedback over marine low cloud regionsThe feedback reduction is primarily due to altered cloud microphysical parameters and a new deep convection trigger functionProcess‐level analysis is conducted to understand the impact of these model modifications on cloud feedbacks [ABSTRACT FROM AUTHOR]

Recently, the siRNAs pathways, and especially reproductive phasiRNAs, have attracted attention in eudicots since their biological roles are still unknown and their biogenesis took different evolutionary pathways compared to monocots. In this work, we used Coffea arabica L., a recent allotetraploid formed from the hybridization of Coffea canephora and C. eugenioides unreduced gametes, to explore microsporogenesis and small RNAs‐related pathways in a eudicot crop. First, we identified the microsporogenesis stages during anther development revealing that pre‐meiosis occurs in anthers of 1.5 mm inside floral buds (FBs), whereas meiosis between 1.5 and 4.2 mm FBs, and post‐meiosis in FBs larger than 4.2 mm. These stages coincide with the Brazilian winter, a period of FBs reduced growth which suggests temperature sensitivity. Next, we identified and quantified the expression of reproductive 21‐ and 24‐nt phasiRNAs during coffee anther development together with their canonical and novel miRNA triggers, and characterized the DCL and AGO families. Our results showed that the pattern of reproductive phasiRNA abundance in C. arabica is unique among described eudicots and the canonical trigger car‐miR2275 is involved in the processing of both 21‐ and 24‐nt phasiRNAs. Fourteen DCL genes were identified, but DCL5, related to phasiRNA biosynthesis in monocots, was not, according to its specificity for monocots. Thus, our work explored the knowledge gap about microsporogenesis and related siRNAs pathways in coffee, contributing to the control of reproductive development and the improvement of fertility in eudicots. [ABSTRACT FROM AUTHOR]

Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS‐E2.1‐G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. There are detectable differences in zonal‐mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model's representation of historical changes, including reversing the forced trends in Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that model groups should strive to include more accurate estimates of these drivers in all‐forcing historical simulations in future coupled model intercomparisons. Plain Language Summary: Simulations of recent historical periods are a key test of climate model reliability and skill. These model simulations require an accounting of all the drivers of climate change. We show that the impact of historical changes in freshwater fluxes from ice sheets and ice shelves on the ocean (through changes in salinity and stratification) are detectable in sea surface temperature and sea ice trends, and help improve the match between the modeled climate changes and observations. We recommend that more accurate estimates of these drivers be included in all climate simulations that do not explicitly model ice sheets and ice shelves. Key Points: The response to anomalous meltwater from ice sheets and shelves is large enough for it to be a forcing in historical climate simulationsWhen the GISS model includes these drivers, Southern Ocean SST and sea ice trends better match observationsSteric and dynamic impacts on regional sea level in parts of the North Atlantic and coastal Antarctica are significant [ABSTRACT FROM AUTHOR]

The climate sensitivity peaks around 310 K in a wide variety of climate models, ranging from idealized single column models to fully comprehensive climate models. Here, we increase CO2 using a clear‐sky three‐dimensional atmospheric model with a radiation scheme which maintains accuracy for high CO2 and temperature levels. In contrast, the Equilibrium Climate Sensitivity (ECS) of our model plateaus around 310 K. We show that this is due to the moistening of the subtropical regions caused by a slowdown in atmospheric circulation, which increases the ECS at very high CO2 values. When relative humidity is fixed, the ECS peak is consistent with single column model results. This work does not rule out that clouds or other complex processes impact the ECS in comprehensive climate models. Though the changes in CO2 here are extreme, this study underlines the importance of changes in atmospheric circulation and relative humidity in quantitative assessments of climate sensitivity. Plain Language Summary: From simple single column models to fully comprehensive three‐dimensional climate models, when we increase CO2 to very high levels, we find a peak in the temperature increase per doubling of CO2 at around 310 K. While we understand why this happens in simple models, we lack an understanding for fully comprehensive climate models. Our study uses an intermediate complexity three‐dimensional climate model to bridge this gap. We find that the peak in climate sensitivity is not as pronounced as in other studies. We show that this is due to large changes in the atmospheric circulation which causes a moistening of the subtropics and an increase in the climate sensitivity. Key Points: Climate sensitivity peaks around 310 K in a variety of climate models, ranging from idealized single column models to comprehensive modelsWe use a clear‐sky GCM to bridge the gap between comprehensive models and single column models for which we have a theory for this peakAt high CO2, changes in atmospheric circulation cause a moistening of the subtropical regions and increase the climate sensitivity [ABSTRACT FROM AUTHOR]

Internally generated radiative feedback at interannual timescales can influence climate sensitivity estimates. This study has clarified the mechanisms of variability in radiative feedback associated with the El Niño and Southern Oscillation based on preindustrial control simulations for Coupled Model Intercomparison Project Phase 6. Radiative feedback showed large modulation with a comparable magnitude to the intermodel uncertainty associated with internal changes in the zonal gradient of the ocean thermocline and sea surface temperature (SST) in the equatorial Pacific on interdecadal timescales. When the interdecadal SST anomalies in the eastern and western equatorial Pacific were simulated as positive and negative, respectively, the statistical properties of the interannual variations were also modified with diminished Walker circulation changes per unit of global mean surface temperature rise. Meanwhile, the enhanced local Hadley circulation led to a middle‐to upper‐level cloud decrease and a low‐level cloud increase in the subtropics, causing enhanced negative radiative feedback as a global average. Plain Language Summary: Global warming is usually estimated based on radiative feedbacks: how the climate system responds to external forcing. Even though internal variability can give non‐negligible uncertainty in its estimate, radiative feedbacks characterized by internal variability on interannual timescales such as El Niño and Southern Oscillation (ENSO) potentially contribute to its evaluation. This study clarifies the mechanism of variability on internally generated radiative feedbacks. Radiative feedbacks varied on interdecadal timescales associated with the pattern changes for the background sea surface temperature on the equatorial Pacific. Such background shift strengthens ENSO, thereby enhanced local Hadley circulation strengthens negative feedback. Key Points: Internally generated radiative feedbacks can fluctuate on interdecadal timescales in accordance with the background shiftThe background shift on the equatorial Pacific works to amplify the El Niño and Southern Oscillation (ENSO)Enhanced local Hadley circulation on the equatorial Pacific related to modulated ENSO strengthens negative feedback [ABSTRACT FROM AUTHOR]

This study investigates how climate sensitivity depends upon the spatial pattern of radiative forcing. Sensitivity experiments using a coupled ocean‐atmosphere model were conducted by adding anomalous incoming solar radiation over the entire globe, Northern Hemisphere mid‐latitudes, Southern Ocean, and tropics. The varied forcing patterns led to highly divergent climate sensitivities. Specifically, the climate is nearly twice as sensitive to Southern Ocean forcing as tropical forcing. Strong coupling between the surface and free troposphere in the tropics increases the inversion strength, leading to smaller cloud feedback in the tropical forcing experiments. In contrast, the extratropics exhibit weaker coupling, a decrease or near‐zero change in the inversion strength, and strong positive cloud feedback. These results contrast with the conventional SST‐pattern effect in which tropical surface temperature changes regulate climate sensitivity. They also have important implications for other potentially asymmetric forcings, such as those from geoengineering, volcanic eruptions, and paleoclimatic changes. Plain Language Summary: The way surface temperature responds to radiative forcing depends on where such forcing is applied. Numerical model integrations show that the global mean surface temperature change is doubled when the forcing is imposed over the Southern Ocean compared to when the forcing is applied in the tropics. Changes in the vertical temperature profiles and clouds contribute to the dependence of surface temperature change on the forcing geographic locations. Key Points: The solar forcing pattern effect is investigated in a coupled ocean‐atmosphere modelClimate sensitivity is doubled from tropical forcing to Southern Ocean forcingThe radiative forcing pattern effect involves changes in lapse rate feedback, cloud feedback, and tropospheric stability [ABSTRACT FROM AUTHOR]

We show that the seasonal cycles of clouds over the mid‐latitude oceans in the Northern Hemisphere are predictors of the responses of clouds to increasing sea‐surface temperatures globally. These regions are therefore "natural laboratories" in which the processes responsible for low‐cloud feedbacks on global scales are observed as seasonal changes in local cloud properties. We use an ensemble of configurations of a global‐climate model to show that the sensitivities of cloud‐radiative anomalies to surface temperature and lower‐tropospheric stability in the "laboratory" regions predict the models' global cloud‐radiative feedbacks. Models with greater changes in low‐clouds between seasons are shown to have stronger negative feedbacks in the mid‐latitudes, and stronger positive feedbacks from the subtropical stratocumulus. The biases in the simulated seasonal cycles, compared to observations, imply that both feedbacks are too weak in the model. The consequences of this for configuring our model to have a lower climate sensitivity are discussed. Plain Language Summary: As the Earth's climate warms, feedbacks from changes in clouds are crucial for determining the rate of warming. In some climate models, positive cloud feedbacks are known to contribute to projected temperature increases which fall outside the range that is considered plausible given our current understanding of the Earth's climate. Methods are needed which directly relate present‐day model errors to projected cloud feedbacks so that we can understand how to improve models and simultaneously obtain plausible feedback strengths. We show that in the Met Office Hadley Centres' climate model, the seasonal cycles of clouds in a few key regions, specifically the poleward ocean basins in the Northern Hemisphere, can be used to predict cloud feedbacks, globally, across a wide range of configurations of the model. These regions, particularly the North‐west Pacific, are "natural laboratories" in which the same processes that are responsible for global, cloud‐feedbacks onto global‐warming rates, occur annually as part of the local seasonal cycle. Studying clouds in these regions and model biases in predictions of these clouds will allow us to directly improve estimates of global climate change. Key Points: Seasonal cycles of clouds constrain cloud feedbacksThe northern ocean basins are natural laboratories for low‐cloud feedbacksThe hot‐model problem in HadGEM3 will not easily be solved by tuning [ABSTRACT FROM AUTHOR]

Plant traits are useful for predicting how species may respond to environmental change and/or influence ecosystem properties. Understanding the extent to which traits vary within species and across climatic gradients is particularly important for understanding how species may respond to climate change. We explored whether climate drives spatial patterns of intraspecific trait variation for three traits (specific leaf area (SLA), plant height, and leaf nitrogen content (Nmass)) across 122 grass species (family: Poaceae) with a combined distribution across six continents. We tested the hypothesis that the sensitivity (i.e. slope) of intraspecific trait responses to climate across space would be related to the species' typical form and function (e.g. leaf economics, stature and lifespan). We observed both positive and negative intraspecific trait responses to climate with the distribution of slope coefficients across species straddling zero for precipitation, temperature and climate seasonality. As hypothesized, variation in slope coefficients across species was partially explained by leaf economics and lifespan. For example, acquisitive species with nitrogen‐rich leaves grew taller and produced leaves with higher SLA in warmer regions compared to species with low Nmass. Compared to perennials, annual grasses invested in leaves with higher SLA yet decreased height and Nmass in regions with high precipitation seasonality (PS). Thus, while the influence of climate on trait expression may at first appear idiosyncratic, variation in trait–climate slope coefficients is at least partially explained by the species' typical form and function. Overall, our results suggest that a species' mean location along one axis of trait variation (e.g. leaf economics) could influence how traits along a separate axis of variation (e.g. plant size) respond to spatial variation in climate. [ABSTRACT FROM AUTHOR]

Background: Ridge and furrow (RIFU) systems and associated soils are a widespread legacy of medieval agriculture, are archives of historical land use, and might affect recent ecosystems. Open questions about RIFU formation and potential legacy effects still exist, especially related to physical soil properties. Aims: Our aims were (1) to characterize the soil properties of RIFU soils and (2) to compare the drought sensitivity and the growth resistance in extremely dry years of trees growing on ridges and furrows, respectively. Methods: We studied soil physical (bulk density, saturated soil hydraulic conductivity, and texture) and chemical (soil pH, soil organic matter, and nitrogen content) properties and the climate sensitivity of tree growth on RIFU systems for three study sites in Prignitz, Germany. Results: RIFU systems showed a high spatial heterogeneity of soil stratigraphy due to ridge construction and increased accumulation of soil moisture and organic matter in furrows due to post‐abandonment pedogenesis. Slight spatial differences in soil physical properties were found, with increased air capacity in ridge soils and higher available water contents in furrow soils. No differences in drought sensitivity were observed for trees growing on ridges and furrows, except for a wet site, where trees in furrows showed a higher sensitivity. Resistance in dry years tended to be similar or increase from furrows to ridges. Conclusions: The results reflect a spatial differentiation of stratigraphy and post‐abandonment pedogenesis on abandoned RIFU systems and suggest an adaption to different moisture conditions through RIFU construction. Differences in drought sensitivity of tree growth with relative land surface could only be detected for one of the three sites, where trees were found to be less drought sensitive on ridges. [ABSTRACT FROM AUTHOR]

Effective climate sensitivity (EffCS), commonly estimated from model simulations with abrupt 4×CO2 for 150 years, has been shown to depend on the CO2 forcing level. To understand this dependency systematically, we performed a series of simulations with a range of abrupt CO2 forcing in two climate models. Our results indicate that normalized EffCS values in these simulations are a non‐monotonic function of the CO2 forcing, decreasing between 3× and 4×CO2 in CESM1‐LE (2× and 3×CO2 in GISS‐E2.1‐G) and increasing at higher CO2 levels. The minimum EffCS value, caused by anomalously negative radiative feedbacks, arises mainly from sea‐surface temperature (SST) relative cooling in the tropical and subtropical North Atlantic. This cooling is associated with the formation of the North Atlantic Warming Hole and Atlantic Meridional Overturning Circulation collapse under CO2 forcing. Our findings imply that understanding changes in North Atlantic SST patterns is important for constraining near‐future and equilibrium global warming. Plain Language Summary: Estimates of effective climate sensitivity (EffCS) are complicated by the nonlinear dependence of feedback on (a) global mean surface temperature and (b) the pattern of sea‐surface temperature. We find that EffCS and radiative feedbacks change non‐monotonically with CO2 concentrations due to a cooling sea‐surface temperature (SST) pattern over the North Atlantic associated with the formation of a "North Atlantic Warming Hole" (NAWH). While most previous studies focused on the impact of tropical Pacific SST patterns on EffCS, we here highlight an overlooked damping effect on EffCS from North Atlantic SST cooling across CO2 levels. Our results imply that understanding and constraining the NAWH under CO2 forcings is crucial for transient warming projections and EffCS constraints. Key Points: Effective climate sensitivity (EffCS) and radiative feedbacks change non‐monotonically with increasing CO2 concentrationsThe non‐monotonicity is associated with the formation of a sea‐surface temperature cooling pattern over the North AtlanticResults imply an overlooked radiative damping effect on global EffCS from the North Atlantic warming hole [ABSTRACT FROM AUTHOR]

Planetary boundary layer (PBL) schemes parameterize unresolved turbulent mixing within the PBL and free troposphere (FT). Previous studies reported that precipitation simulation over the Amazon in South America is quite sensitive to PBL schemes and the exact relationship between the turbulent mixing and precipitation processes is, however, not disentangled. In this study, regional climate simulations over the Amazon in January–February 2019 are examined at process level to understand the precipitation sensitivity to PBL scheme. The focus is on two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, which show the largest difference in the simulated precipitation. During daytime, while the FT clouds simulated by YSU dissipate, clouds simulated by ACM2 maintain because of enhanced moisture supply due to the enhanced vertical moisture relay transport process: (a) vertical mixing within PBL transports surface moisture to the PBL top, and (b) FT mixing feeds the moisture into the FT cloud deck. Due to the thick cloud deck over Amazon simulated by ACM2, surface radiative heating is reduced and consequently the convective available potential energy is reduced. As a result, precipitation is weaker from ACM2. Two key parameters dictating the vertical mixing are identified, p, an exponent determining boundary layer mixing and λ, a scale dictating FT mixing. Sensitivity simulations with altered p, λ, and other treatments within YSU and ACM2 confirm the precipitation sensitivity. The FT mixing in the presence of clouds appears most critical to explain the sensitivity between YSU and ACM2. Plain Language Summary: Predictions of weather and climate in terms of clouds and precipitation over the Amazon in South America are quite uncertain. This uncertainty has been largely attributed to errors in the planetary boundary layer (PBL) scheme, which represents turbulent mixing. A lack of understanding of the relationship between turbulence, clouds, and precipitation processes prevents us from improving PBL representation in models to achieve better weather and climate simulations. This study disentangles the turbulence/clouds/precipitation relationship, and identifies the root cause of model errors in PBL schemes using regional climate simulations over the Amazon. Two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, are examined, which show the largest difference in the simulated precipitation. The main difference between the two PBL schemes is the dissipation (YSU) or maintenance (ACM2) of clouds during daytime above the boundary layer, which modulates surface heating and consequently precipitation. The maintenance of a thick cloud deck over the Amazon in ACM2, is caused by enhanced vertical transport of moisture from the surface to above the boundary layer. Such an improved understanding of the turbulence/clouds/precipitation relationship allow us to propose potential solutions to improve PBL schemes in weather and climate models. Key Points: Disentangle turbulence/cloud/precipitation processes over Amazon and reveal root cause for sensitivity to planetary boundary layer (PBL) schemes using the Weather Research and Forecasting modelFree troposphere (FT) mixing becomes prominent in the presence of clouds, which in turn supports maintenance of the FT clouds that would otherwise dissipateStronger vertical moisture relay transport in asymmetric convective model v2 (ACM2) PBL scheme supports thicker FT clouds, leading to reduced heating and precipitation [ABSTRACT FROM AUTHOR]

Divergence in seed germination patterns among populations of the same species is important for understanding plant responses to environmental gradients and potential plant sensitivity to climate change. In order to test responses to flooding and decreasing water potentials, over 3 years we germinated and grew seeds from three habitats of Euterpe edulis Mart. occurring along an altitudinal gradient.Seed germination and root growth were evaluated under different water availability treatments: control, flood, −0.4 MPa, −0.8 MPa, in the years 2012, 2013 and 2014, and in the final year of the experiment (2014) at −1.0 MPa and −1.5 MPa.Seeds from the montane habitat did not germinate in the flooding treatment. Seed germination of all three habitats decreased in the −1.5 MPa treatment and the montane habitat had lowest germination in this treatment. Time required for half of the seeds to germinate increased up to −0.8 MPa. Seeds from montane habitats germinated more slowly in all treatments. The only difference in seed germination synchrony was an increase in the submontane population under the flooding treatment. However, synchrony decreased at the lowest water potentials. Roots of the montane population were more vigorous in most treatments, except at −0.8 MPa.The unusual ability of these seeds to germinate at low water potentials might be related to early seed germination at the onset of the rainy season, which potentially decreases seed predation pressure. Seeds of the montane population were more sensitive to both types of water stress. A predicted increase in the frequency and intensity of extreme high rainfall or drought events may predispose early stages of this population to adverse factors that might negatively affect population viability with elevational in future climate change scenarios. [ABSTRACT FROM AUTHOR]

Climate sensitivity of infectious diseases is discussed in many studies. A quantitative basis for distinguishing and predicting the disease impacts of climate and other environmental and anthropogenic driver‐pressure changes, however, is often lacking. To assess research effort and identify possible key gaps that can guide further research, we here apply a scoping review approach to two widespread infectious diseases: Lyme disease (LD) as a vector‐borne and cryptosporidiosis as a water‐borne disease. Based on the emerging publication data, we further structure and quantitatively assess the driver‐pressure foci and interlinkages considered in the published research so far. This shows important research gaps for the roles of rarely investigated water‐related and socioeconomic factors for LD, and land‐related factors for cryptosporidiosis. For both diseases, the interactions of host and parasite communities with climate and other driver‐pressure factors are understudied, as are also important world regions relative to the disease geographies; in particular, Asia and Africa emerge as main geographic gaps for LD and cryptosporidiosis research, respectively. The scoping approach developed and gaps identified in this study should be useful for further assessment and guidance of research on infectious disease sensitivity to climate and other environmental and anthropogenic changes around the world. Plain Language Summary: This study looks at how the effects of changes in climate and other environmental and anthropogenic factors are addressed in research on two widespread infectious diseases: Lyme disease (LD) (tick‐borne) and cryptosporidiosis (water‐borne). By a scoping review of existing research publications, the study identifies key gaps that need to be bridged in future research. For LD, more research is needed on water‐related and socioeconomic factors and their climate linkages, while for cryptosporidiosis, land‐related factors and their linkages with climate need further exploration. For both diseases, the interactions between host and parasite communities and their changes along with those in climate and other environmental factors emerge as insufficiently studied. Geographically, research is lacking for LD in Asia and cryptosporidiosis in Africa. By identifying and highlighting these research gaps, this study aims to guide future research on how changes in climate and other factors, and their interlinkages affect these infectious diseases around the world. Key Points: Studies on Lyme disease (LD) and cryptosporidiosis relationships with climate lack coupling with other environmental and anthropogenic factorsDriver‐pressure interactions with host and parasite communities are generally understudiedAsia and Africa are main geographic research gaps for LD and cryptosporidiosis research, respectively [ABSTRACT FROM AUTHOR]

Many global climate models underestimate the cloud cover and overestimate the cloud albedo, especially for low‐level clouds. We determine how a correct representation of the vertical structure of clouds can fix part of this bias. We use the 1D McICA framework and focus on low‐level clouds. Using Large Eddy Simulations results as reference, we propose a method based on exponential‐random overlap that represents the cloud overlap between layers and the subgrid cloud properties over several vertical scales, with a single value of the overlap parameter. Starting from a coarse vertical grid, representative of atmospheric models, this algorithm is used to generate the vertical profile of the cloud fraction with a finer vertical resolution, or to generate it on the coarse grid but with subgrid heterogeneity and cloud overlap that ensures a correct cloud cover. Doing so we find decorrelation lengths are dependent on the vertical resolution, except if the vertical subgrid heterogeneity and interlayer overlap are taken into account coherently. We confirm that the frequently used maximum‐random overlap leads to a significant error by underestimating the low‐level cloud cover with a relative error of about 50%, that can lead to an error of SW cloud albedo as big as 70%. Not taking into account the subgrid vertical heterogeneity of clouds can cause a relative error of 20% in brightness, assuming the cloud cover is correct. Plain Language Summary: Low‐level clouds are the main source of spread in model estimates of climate sensitivity, but climate models resolutions do not allow them to explicitly resolve the geometrical complexity of low‐level clouds, which must be parametrized. Most climate models low‐level clouds have a cloud cover too small and a cloud albedo too high, which is known as the "too few too bright bias." In this work we determine whether a better representation of the vertical structure of clouds can fix part of this bias. We use high‐resolution simulations as references and radiative transfer algorithms to assess the performances of our cloud generation. We use a framework of commonly used overlap assumptions, and we extend it to also represent vertical subgrid variability. When the cloud cover of the scene is known, we show that an appropriate choice of parameters allows a good representation of the vertical structure of clouds and of the cloud albedo. We find the parameters used to model the overlap are highly dependent on the model vertical resolution, and present a way to overcome this dependency when both subgrid scale and interlayer overlap are taken into account consistently. Key Points: We extend the use of exponential‐random overlap (ERO) to represent both overlap and subgrid variabilityThe commonly used maximum‐random overlap hypothesis can generate cloud covers half too smallThe decorrelation lengths used with ERO are highly dependent on the vertical resolutions of models and observations [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]