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In the coming decades, climate change is expected to dramatically affect communities worldwide, altering the patterns of many ambient exposures and disasters, including extreme temperatures, heat waves, wildfires, droughts, and floods. These exposures, in turn, can affect risks for a variety of human diseases and health outcomes. Climate epidemiology plays an important role in informing policy related to climate change and its threats to public health. Climate epidemiology leverages deep, integrated collaborations between epidemiologists and climate scientists to understand the current and potential future impacts of climate-related exposures on human health. A variety of recent and ongoing developments in climate science are creating new avenues for epidemiologic contributions. Here, we discuss the contributions of climate epidemiology and describe some key current research directions, including research to better characterize uncertainty in climate health projections. We end by outlining 3 developing areas of climate science that are creating opportunities for high-impact epidemiologic advances in the near future: 1) climate attribution studies, 2) subseasonal to seasonal forecasts, and 3) decadal predictions., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Periodic behavior in the climate system has important implications not only for weather prediction but also for understanding and interpreting the physical processes that drive climate variability. Here we demonstrate that the large-scale Southern Hemisphere atmospheric circulation exhibits marked periodicity on time scales of approximately 20 to 30 days. The periodicity is tied to the Southern Hemisphere baroclinic annular mode and emerges in hemispheric-scale averages of the eddy fluxes of heat, the eddy kinetic energy, and precipitation. Observational and theoretical analyses suggest that the oscillation results from feedbacks between the extratropical baroclinicity, the wave fluxes of heat, and radiative damping. The oscillation plays a potentially profound role in driving large-scale climate variability throughout much of the mid-latitude Southern Hemisphere.

Superstorm Sandy ravaged the eastern seaboard of the United States, costing a great number of lives and billions of dollars in damage. Whether events like Sandy will become more frequent as anthropogenic greenhouse gases continue to increase remains an open and complex question. Here we consider whether the persistent large-scale atmospheric patterns that steered Sandy onto the coast will become more frequent in the coming decades. Using the Coupled Model Intercomparison Project, phase 5 multimodel ensemble, we demonstrate that climate models consistently project a decrease in the frequency and persistence of the westward flow that led to Sandy's unprecedented track, implying that future atmospheric conditions are less likely than at present to propel storms westward into the coast.

Introduction: Rapid reductions in Arctic sea ice in response to warming have led to increased interest in using the Arctic Ocean for commercial shipping. As the world warms, however, different strategies are being considered to stabilize or reduce surface temperatures in order to prevent critical climate change impacts. One such strategy is stratospheric aerosol injection (SAI), a form of solar climate intervention. Projected changes to Arctic sea ice under SAI with specific regards to shipping have not yet been assessed. Methods: We compare output from two SAI simulations that have different global mean temperature targets with a non-SAI control simulation to provide the first assessment of Arctic Ocean navigability under potential SAI scenarios. Results: We find that sea ice concentration and thickness quickly stabilize or increase after SAI deployment. When sea ice thickness stabilizes in response to SAI, the number of days when the Arctic Ocean is navigable remains fairly constant, but increasing sea ice thickness leads to reduced navigability compared to the non-SAI simulation. From 2035-2069, both the Northwest Passage and Northern Sea Route are accessible from July-November in all three simulations, but there are no navigable routes under either SAI scenario from April-June. When the Arctic is navigable, it can take 2-12 days longer to cross the Arctic Ocean in the SAI simulations than in the non-SAI control simulation, and there are large year-to-year variations in travel time. Discussion: Overall, Arctic shipping may take longer and be more difficult in an SAI vs a non-SAI world because of relatively thicker sea ice, but the degree to which Arctic shipping may change in response to SAI is dependent on the particular climate intervention strategy. [ABSTRACT FROM AUTHOR]

Earth system models are powerful tools to simulate the climate response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement a tool from control theory, called a controller, to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5°C above pre‐industrial temperatures. This work explores how internal (unforced) climate variability can impact controller‐determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE‐SAI) simulations. Since the ARISE‐SAI controller determines injection amounts by comparing global annual‐mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE‐SAI controller and data from Earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability. Plain Language Summary: Stratospheric aerosol injection (SAI) is a proposed climate intervention strategy that injects aerosols into the stratosphere to mitigate some climate change impacts. Several studies that have used climate models to investigate how the atmosphere may respond to SAI implement control algorithms to determine how much aerosol to inject and where in order to achieve certain climate targets. This work explores how changes to the controller input can impact the amount of aerosol injected. Here we focus on the controller from the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE‐SAI) simulations. This specific controller uses the annual‐mean surface temperature to determine how much aerosol to inject. Therefore, internal variability that impacts temperature can impact the total injection amount as well. To quantify how patterns of internal variability impact how much aerosol is injected, we isolate the ARISE‐SAI controller and pass a variety of temperature patterns into it. While this method ignores some interactions between the controller and the climate simulation, it is a quick way to quantify the controller's sensitivity to a large variety of temperature patterns without additional simulations. Key Points: We quantify how the ARISE‐SAI controller responds to different patterns of internal variabilityThe impact from internal variability on the controller‐determined injection is dependent on the background warmingThis method provides a straight‐forward way to efficiently quantify controller sensitivity to internal variability [ABSTRACT FROM AUTHOR]

Stratospheric aerosol injection (SAI) is a proposed form of climate intervention that would release reflective particles into the stratosphere, thereby reducing solar insolation and cooling the planet. The climate response to SAI is not well understood, particularly on short‐term time horizons frequently used by decision‐makers and planning practitioners to assess climate information. We demonstrate two framings to explore the climate response in the decade after SAI deployment in modeling experiments with parallel SAI and no‐SAI simulations. The first framing, which we call a snapshot around deployment, displays change over time within the SAI scenarios and applies to the question "What happens before and after SAI is deployed in the model?" The second framing, the intervention impact, displays the difference between the SAI and no‐SAI simulations, corresponding to the question "What is the impact of a given intervention relative to climate change with no intervention?" We apply these framings to annual mean 2 m temperature, precipitation, and a precipitation extreme during the 10 yr after deployment in two large ensembles of Earth system model simulations that comprehensively represent both the SAI injection process and climate response, and connect these results to implications for other climate variables. We show that SAI deployment robustly reduces changes in many high‐impact climate variables even on these short timescales where the forced response is relatively small, but that details of the climate response depend on the model version, greenhouse gas emissions scenario, and other aspects of the experimental design. Key Points: We demonstrate two ways to frame results from modeling experiments with parallel stratospheric aerosol injection (SAI) and no‐SAI large ensemblesSAI deployment could minimize changes in many high‐impact climate variables across spatial scales on policy‐relevant time horizonsResults are scenario‐ and model‐dependent so consistency among different SAI simulations does not imply truth for any general SAI deployment [ABSTRACT FROM AUTHOR]

Leveraging artificial neural networks (ANNs) trained on climate model output, we use the spatial pattern of historical temperature observations to predict the time until critical global warming thresholds are reached. Although no observations are used during the training, validation, or testing, the ANNs accurately predict the timing of historical global warming from maps of historical annual temperature. The central estimate for the 1.5 °C global warming threshold is between 2033 and 2035, including a ± 1σ range of 2028 to 2039 in the Intermediate (SSP2-4.5) climate forcing scenario, consistent with previous assessments. However, our data-driven approach also suggests a substantial probability of exceeding the 2 °C threshold even in the Low (SSP1-2.6) climate forcing scenario. While there are limitations to our approach, our results suggest a higher likelihood of reaching 2 °C in the Low scenario than indicated in some previous assessments--though the possibility that 2 °C could be avoided is not ruled out. Explainable AI methods reveal that the ANNs focus on particular geographic regions to predict the time until the global threshold is reached. Our framework provides a unique, data-driven approach for quantifying the signal of climate change in historical observations and for constraining the uncertainty in climate model projections. Given the substantial existing evidence of accelerating risks to natural and human systems at 1.5 °C and 2 °C, our results provide further evidence for high-impact climate change over the next three decades. [ABSTRACT FROM AUTHOR]

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 Kdecade-1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 Kdecade-1. Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 Kdecade-1 . The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models. [ABSTRACT FROM AUTHOR]

As anthropogenic activities continue to drive increases in extreme events, the fundamental solution of reducing greenhouse gas emissions remains elusive. Thus, there is growing interest in stratospheric aerosol injection (SAI) to offset some of the most dangerous consequences of climate change. Although global SAI deployment would likely be easy to detect by some metrics, the detectability of SAI on extreme events might be more difficult. We examine this question in climate model simulations of SAI; specifically, the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS‐SAI) scenario. We train a logistic regression model to predict whether a map of global extremes came from climate simulations with or without SAI. The timing of accurate predictions is a quantification of the time required to detect SAI impacts. We find that regional changes in extreme temperature and precipitation under GLENS are robustly detected within 1 and 15 years of initial SAI injection, respectively. Plain Language Summary: In light of concerns regarding increasing extremes driven by human‐induced climate change, and the limited progress to‐date of climate change solutions, a key recommendation from a recent National Academies of Science, Engineering and Medicine report is that the United States should establish a transdisciplinary research program into proposed climate intervention techniques, including stratospheric aerosol injection (SAI). SAI would increase the number of small reflective particles in the upper atmosphere to cool the climate by reflecting a small percentage of incoming solar radiation back into space. If SAI were deployed, the question arises as to when and where we might first detect regional impacts of SAI on climate extremes. Here, we begin to examine this question by analyzing climate model simulations of the 21st century both without and with SAI deployment. We train a simple statistical method to predict whether a map of climate extremes came from a world with or without SAI. By looking at the ability of the statistical model to accurately identify the presence (or absence) of SAI deployment, we find that regional changes in extreme temperatures and precipitation under SAI are robustly detected within 1 and 15 years of initial SAI deployment, respectively. Key Points: A statistical model is trained to predict whether a map of global extremes came from a Representative Concentration Pathway 8.5 or stratospheric aerosol injection (SAI) simulationThe timing of accurate predictions acts as a quantification of the time to detection of a geoengineered climateRegional changes in extreme temperatures and extreme precipitation under SAI are robustly detected within 1 and 15 years of injection [ABSTRACT FROM AUTHOR]

As anthropogenic activities warm the Earth, the fundamental solution of reducing greenhouse gas emissions remains elusive. Given this mitigation gap, global warming may lead to intolerable climate changes as adaptive capacity is exceeded. Thus, there is emerging interest in solar radiation modification, which is the process of deliberately increasing Earth’s albedo to cool the planet. Stratospheric aerosol injection (SAI)—the theoretical deployment of particles in the stratosphere to enhance reflection of incoming solar radiation—is one strategy to slow, pause, or reverse global warming. If SAI is ever pursued, it will likely be for a specific aim, such as affording time to implement mitigation strategies, lessening extremes, or reducing the odds of reaching a biogeophysical tipping point. Using an ensemble climate model experiment that simulates the deployment of SAI in the context of an intermediate greenhouse gas trajectory, we quantified the probability that internal climate variability masks the effectiveness of SAI deployment on regional temperatures. We found that while global temperature was stabilized, substantial land areas continued to experience warming. For example, in the SAI scenario we explored, up to 55% of the global population experienced rising temperatures over the decade following SAI deployment and large areas exhibited high probability of extremely hot years. These conditions could cause SAI to be perceived as a failure. Countries with the largest economies experienced some of the largest probabilities of this perceived failure. The potential for perceived failure could therefore have major implications for policy decisions in the years immediately following SAI deployment. [ABSTRACT FROM AUTHOR]

Subseasonal timescales (∼2 weeks–2 months) are known for their lack of predictability, however, specific Earth system states known to have a strong influence on these timescales can be harnessed to improve prediction skill (known as "forecasts of opportunity"). As the climate continues warming, it is hypothesized these states may change and consequently, their importance for subseasonal prediction may also be impacted. Here, we examine changes to midlatitude subseasonal prediction skill provided by the tropics under anthropogenic warming using artificial neural networks to quantify skill. The network is tasked to predict the sign of the 500 hPa geopotential height for historical and future time periods in the Community Earth System Model Version 2 ‐ Large Ensemble across the Northern Hemisphere at a 3 week lead using tropical precipitation. We show prediction skill changes substantially in key midlatitude regions and these changes appear linked to changes in seasonal variability with the largest differences in accuracy occurring during forecasts of opportunity. Plain Language Summary: Predictions on 2 weeks to 2 months (subseasonal) timescales are important for the public and private sectors due to the increased preparation time provided to save lives and property. In the current climate, signals initiated in the tropics can overpower noise in the midlatitudes and ultimately lead to enhanced midlatitude subseasonal prediction skill. However, it has been hypothesized that increasing global temperatures due to climate change may impact these signals and their sources in the future. Therefore, it is important to understand how subseasonal predictability provided by the tropics will be affected. Here, we utilize a type of machine learning known as a neural network to investigate this question. We find that subseasonal prediction skill provided by the tropics changes throughout the Northern Hemisphere in a warmer climate and these changes appear mainly linked to changes in seasonal variability. In addition, we see that the largest differences in accuracy occur during opportunities for enhanced subseasonal prediction skill. Key Points: Neural networks can be used to evaluate subseasonal predictability under future climate change scenariosIn community earth system model version 2 ‐ Large ensemble (CESM2‐LE), largest differences in subseasonal predictability provided by the tropics mainly occur during forecasts of opportunityChanges in Northern Hemisphere subseasonal prediction skill appear mainly linked to changes to seasonal variability in CESM2‐LE [ABSTRACT FROM AUTHOR]

Evaluating historical simulations from global climate models (GCMs) remains an important exercise for better understanding future projections of climate change and variability in rapidly warming regions, such as the Arctic. As an alternative approach for comparing climate models and observations, we set up a machine learning classification task using a shallow artificial neural network (ANN). Specifically, we train an ANN on maps of annual mean near‐surface temperature in the Arctic from a multi‐model large ensemble archive in order to classify which GCM produced each temperature map. After training our ANN on data from the large ensembles, we input annual mean maps of Arctic temperature from observational reanalysis and sort the prediction output according to increasing values of the ANN's confidence for each GCM class. To attempt to understand how the ANN is classifying each temperature map with a GCM, we leverage a feature attribution method from explainable artificial intelligence. By comparing composites from the attribution method for every GCM classification, we find that the ANN is learning regional temperature patterns in the Arctic that are unique to each GCM relative to the multi‐model mean ensemble. In agreement with recent studies, we show that ANNs can be useful tools for extracting regional climate signals in GCMs and observations. Plain Language Summary: Due to many complex processes in the climate system, the Arctic is warming more rapidly relative to other parts of the globe. To understand the impacts of these changes in the Arctic, it is important to evaluate climate model projections. While there are other existing statistical methods for assessing simulations between different climate models, we introduce a machine learning approach for comparing climate models and observations using a tool called artificial neural networks (ANNs). We set up our problem by inputting yearly maps of temperature in the Arctic and then task the ANN to classify which climate model produced each map. To understand how the ANN learns where the temperature map is coming from, we utilize a visualization method to peer into the machine learning black box. After training our ANN on data from different climate models, we then input maps of Arctic temperature from observations to evaluate which climate model is classified for every year in the historical record. Using this setup, we find that the ANN is leveraging regional patterns of temperatures, and not just overall warm and cold biases, in order to make its climate model and observation predictions. Key Points: An artificial neural network is trained to identify which climate model produced an annual mean map of near‐surface temperature in the ArcticThe classification network is evaluated using input from atmospheric reanalysis as a method of comparing climate models and observationsAn explainability method reveals regional temperature patterns the artificial neural network is using to classify observations with different climate models [ABSTRACT FROM AUTHOR]

Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These "indicator patterns" vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields. Plain Language Summary: Using machine learning tools called neural networks, we identify patterns of the changing climate within climate model data. Changes in the climate can be identified earlier when detecting patterns within maps of multiple variables and seasons than for single maps alone. By visualizing the patterns learned by the neural networks, we can identify which regions, variables, and seasons are most important for detecting climate change. These patterns offer insight into how climate change is represented in different climate models, and how the patterns of climate change will evolve over time. Key Points: Neural networks and their explainability tools can be harnessed to identify patterns of forced change within combined fieldsCombined fields of input allow for earlier detection of the emergence of a forced climate responseExplainable AI techniques can be used to identify patterns that describe the emergence and evolution of forced climate change [ABSTRACT FROM AUTHOR]

The global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long‐term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content (OHC) at the onset. Through a machine learning explainability method, we find the ANN is learning off‐equatorial patterns of anomalous OHC that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations. Plain Language Summary: Long‐term observations reveal that Earth's average temperature is rising due to human‐caused climate change. Along with this warming trend are also variations from year‐to‐year and even over multiple decades. This temperature variability is often tied to regional patterns of heat in the deep ocean, which can then modulate weather and climate extremes over land. In an attempt to better predict temperature variability on decadal timescales, we use a machine learning method called artificial neural networks and data from a climate model experiment, which was designed to compare climate change and variability. Here, our artificial neural network (ANN) uses maps of ocean heat to predict the onset of temporary slowdowns in the rate of global warming in both the climate model and in real‐world observations. We then use a visualization technique to find which areas of ocean heat that the ANN is using to make its correct predictions, which are found to be mainly across the Pacific Ocean. In agreement with recent research, our study finds that new data science methods, like machine learning, can be useful tools for predicting variations in global climate. Key Points: An artificial neural network predicts the onset of slowdowns in decadal warming trends of global mean surface temperatureExplainable AI reveals the neural network is leveraging tropical patterns of ocean heat content anomalies to make its predictionsTransitions in the phase of the Interdecadal Pacific Oscillation are frequently associated with warming slowdown predictions in CESM2‐LE [ABSTRACT FROM AUTHOR]

It remains difficult to disentangle the relative influences of aerosols and greenhouse gases on regional surface temperature trends in the context of global climate change. To address this issue, we use a new collection of initial‐condition large ensembles from the Community Earth System Model version 1 that are prescribed with different combinations of industrial aerosol and greenhouse gas forcing. To compare the climate response to these external forcings, we adopt an artificial neural network (ANN) architecture from previous work that predicts the year by training on maps of near‐surface temperature. We then utilize layer‐wise relevance propagation (LRP) to visualize the regional temperature signals that are important for the ANN's prediction in each climate model experiment. To mask noise when extracting only the most robust climate patterns from LRP, we introduce a simple uncertainty metric that can be adopted to other explainable artificial intelligence (AI) problems. We find that the North Atlantic, Southern Ocean, and Southeast Asia are key regions of importance for the neural network to make its prediction, especially prior to the early‐21st century. Notably, we also find that the ANN predictions based on maps of observations correlate higher to the actual year after training on the large ensemble experiment with industrial aerosols held fixed to 1920 levels. This work illustrates the sensitivity of regional temperature signals to changes in aerosol forcing in historical simulations. By using explainable AI methods, we have the opportunity to improve our understanding of (non)linear combinations of anthropogenic forcings in state‐of‐the‐art global climate models. Plain Language Summary: Using a machine learning method called artificial neural networks (ANNs), we explore how human‐caused climate drivers can affect regional patterns of surface temperature. Here we use a climate model with different combinations of greenhouse gases and industrial aerosols (particles in the atmosphere) to understand their influence on climate change and variability. By employing visualization tools to see how the ANN makes its predictions, we can better recognize how these climate drivers influence global temperature in the past, present, and future. For instance, we find that aerosols emitted in the 20th century and early 21st century have influenced global warming temperature trends in some areas of the world, such as over the North Atlantic Ocean. Machine learning accompanied by new visualization methods have the potential to bring new insights into understanding the effects of global climate change in observations and models. Key Points: Using explainable artificial intelligence methods with artificial neural networks (ANN) reveals climate patterns in large ensemble simulationsPredictions from an ANN trained using a large ensemble without time‐evolving aerosols show the highest correlation with actual observationsA metric is proposed for quantifying the uncertainty of an ANN visualization method that extracts signals from different external forcings [ABSTRACT FROM AUTHOR]

While the Madden‐Julian oscillation (MJO) is known to influence the midlatitude circulation and its predictability on subseasonal‐to‐seasonal timescales, little is known how this connection may change with anthropogenic warming. This study investigates changes in the causal pathways between the MJO and the North Atlantic oscillation (NAO) within historical and SSP585 simulations of the Community Earth System Model 2‐Whole Atmosphere Community Climate Model (CESM2‐WACCM) coupled climate model. Two data‐driven approaches are employed, namely, the STRIPES index and graphical causal models. These approaches collectively indicate that the MJO's influence on the North Atlantic strengthens in the future, consistent with an extended jet‐stream. In addition, the graphical causal models allow us to distinguish the causal pathways associated with the teleconnections. While both a stratospheric and tropospheric pathway connect the MJO to the North Atlantic in CESM2‐WACCM, the strengthening of the MJO‐NAO causal connection over the 21st century is shown to be due exclusively to teleconnections via the tropospheric pathway. Plain Language Summary: Climate models are useful tools for obtaining better understanding of the complex mechanisms that govern Earth's climate, as well as better understanding of the impacts of climate change. This study focuses on using the Community Earth System Model 2‐Whole Atmosphere Community Climate Model (CESM2‐WACCM) climate model output along with several data‐driven approaches to gain such insights. We focus specifically on the Madden‐Julian oscillation (MJO), an atmospheric phenomenon of regions of stormy air and dry air that slowly progresses around the globe in the tropics. The MJO is well‐known to have an influence on the weather and climate of many parts of the world, including the North Atlantic. Our study finds that the CESM2‐WACCM climate model correctly simulates connections between the MJO and the North Atlantic and that this influence strengthens over the 21st century. These insights have important implications for understanding how our ability to forecast weather over Europe and eastern North America may change in the coming decades. Key Points: CESM2‐WACCM captures both the stratospheric and tropospheric pathways connecting the Madden‐Julian oscillation (MJO) to the North AtlanticMJO teleconnections to the North Atlantic strengthen in the future under SSP585 forcingThe strengthening of the MJO‐NAO connection is due to the tropospheric pathway while the stratospheric pathway changes very little [ABSTRACT FROM AUTHOR]

Many problems in climate science require extracting forced signals from a background of internal climate variability. We demonstrate that artificial neural networks (ANNs) are a useful addition to the climate science "toolbox" for this purpose. Specifically, forced patterns are detected by an ANN trained on climate model simulations under historical and future climate scenarios. By identifying spatial patterns that serve as indicators of change in surface temperature and precipitation, the ANN can determine the approximate year from which the simulations came without first explicitly separating the forced signal from the noise of both internal climate variability and model uncertainty. Thus, the ANN indicator patterns are complex, nonlinear combinations of signal and noise and are identified from the 1960s onward in simulated and observed surface temperature maps. This approach suggests that viewing climate patterns through an artificial intelligence (AI) lens has the power to uncover new insights into climate variability and change. Plain Language Summary: Many problems in climate science require the identification of signals amidst a sea of climate "noise" and across a variety of models which can disagree with one another. Here, we demonstrate that machine learning techniques, specifically artificial neural networks, can help identify forced patterns of temperature and precipitation within climate model simulations as well as the observations. In fact, the neural network is able to identify patterns of forced change of surface temperature as early as the 1960s in climate model simulations. The results shown here are strongly suggestive of the potential power of machine learning for climate research. Key Points: Neural networks can identify forced patterns of surface temperature and precipitation amidst climate noise and model disagreementThese "indicator patterns" of forced change are present in the observationsViewing climate patterns through an AI lens has the power to uncover new insights into climate variability and change [ABSTRACT FROM AUTHOR]

Understanding the response of atmospheric blocking events to climate change has been of great interest in recent years. However, potential changes in the blocking area (size), which can affect the spatiotemporal characteristics of the resulting extreme events, have not received much attention. Using two large‐ensemble, fully coupled general circulation model (GCM) simulations, we show that the size of blocking events increases with climate change, particularly in the Northern Hemisphere (by as much as 17%). Using a two‐layer quasi‐geostrophic model and a dimensional analysis technique, we derive a scaling law for the size of blocking events, which shows that area mostly scales with width of the jet times the Kuo scale (i.e., the length of stationary Rossby waves). The scaling law is validated in a range of idealized GCM simulations. Predictions of this scaling law agree well with changes in blocking events' size under climate change in fully coupled GCMs in winters but not in summers. Key Points: Size of blocking events robustly increases with climate change in most regions in two sets of large‐ensemble fully coupled GCM simulationsA scaling law for blocking area is derived in a QG model using the Buckingham‐π theorem and is verified using idealized GCM simulationsThe scaling law is area ~ width of the jet × Kuo scale and partially explains the projected changes in the fully coupled GCM simulations [ABSTRACT FROM AUTHOR]

Previous studies have suggested that, in the zonal mean, the climatological Northern Hemisphere wintertime eddy-driven jet streams will weaken and shift equatorward in response to Arctic amplification and sea ice loss. However, multiple studies have also pointed out that this response has strong regional differences across the two ocean basins, with the North Atlantic jet stream generally weakening across models and the North Pacific jet stream showing signs of strengthening. Based on the zonal wind response with a fully coupled model, this work sets up two case studies using a barotropic model to test a dynamical mechanism that can explain the differences in zonal wind response in the North Pacific versus the North Atlantic. Results indicate that the differences between the two basins are due, at least in part, to differences in the proximity of the jet streams to the sea ice loss, and that in both cases the eddies act to increase the jet speed via changes in wave breaking location and frequency. Thus, while baroclinic arguments may account for an initial reduction in the midlatitude winds through thermal wind balance, eddy–mean flow feedbacks are likely instrumental in determining the final total response and actually act to strengthen the eddy-driven jet stream. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) often generate extreme precipitation, with AR temperature strongly influencing hydrologic impacts by altering the timing and magnitude of runoff. Long‐term changes in AR temperatures therefore have important implications for regional hydroclimate—especially in locations where a shift to more rain‐dominated AR precipitation could affect flood risk and/or water storage in snowpack. In this study, we provide the first climatology of AR temperature across five U.S. West Coast subregions. We then assess trends in landfalling AR temperatures for each subregion from 1980 to 2016 using three reanalysis products. We find AR warming at seasonal and monthly scales. Cool‐season warming ranges from 0.69 to 1.65 °C over the study period. We detect monthly scale warming of >2 °C, with the most widespread warming occurring in November and March. To understand the causes of AR warming, we quantify the density of AR tracks from genesis to landfall and analyze along‐track AR temperature for each month and landfall region. We investigate three possible influences on AR temperature trends at landfall: along‐track temperatures prior to landfall, background temperatures over the landfall region, and AR temperature over the coastal ocean adjacent to the region of landfall. Generally, AR temperatures at landfall more closely match coastal and background temperature trends than along‐track AR temperature trends. The seasonal asymmetry of the AR warming and the heterogeneity of influences have important implications for regional water storage and flood risk—demonstrating that changes in AR characteristics are complex and may not be directly inferred from changes in the background climate. Plain Language Summary: Atmospheric river (AR) storms are well known for their ability to accumulate snowpack, provide drought relief, and generate extreme precipitation and flooding along the West Coast of the United States. AR temperature is an important variable for determining the water resource impacts of a given event, such as the ratio of rain to snow delivered by an individual storm. As a result, changes in AR temperature have implications for both water storage and flood risk. We find substantial warming in ARs at both the seasonal and monthly scales, as well as seasonal and regional variations in the amount of warming along the U.S. West Coast. To understand the warming of ARs at the landfall regions, we compare these trends with trends in temperature along the AR tracks, background temperature over the landfall region, and temperature over the coastal ocean adjacent to the landfall region. The most robust warming occurs in November and March, which has important implications for increased regional flood risk and decreased water storage, and motivates further investigation in other AR‐prone regions around the globe. Key Points: All U.S. West Coast subregions exhibit positive atmospheric river (AR) temperature trends, with warming ranging from 0.69 to 1.65 °C over the study periodAR warming magnitude exhibits seasonal and regional asymmetry; ARs have warmed by more than 2 °C in some months, particularly in MarchAR warming at landfall is influenced by temperature trends over the coastal ocean, over the landfall region, and along the AR tracks [ABSTRACT FROM AUTHOR]

This study introduces a simple analytic expression for calculating the lead time required for a linear trend to emerge in a Gaussian first-order autoregressive process. The expression is derived from the standard error of the regression and is tested using the NCAR Community Earth System Model Large Ensemble of climate change simulations. It is shown to provide a robust estimate of the point in time when the forced signal of climate change has emerged from the natural variability of the climate system with a predetermined level of statistical confidence. The expression provides a novel analytic tool for estimating the time of emergence of anthropogenic climate change and its associated regional climate impacts from either observed or modeled estimates of natural variability and trends. [ABSTRACT FROM AUTHOR]

A dry dynamical core is used to investigate the seasonal sensitivity of the circulation to two idealized thermal forcings: a tropical upper-tropospheric heating and a polar lower-tropospheric heating. The thermal forcings are held constant, and the response of the circulation in each month of the year is explored. First, the circulation responses to tropical warming and polar warming are studied separately, and then the response to the simultaneously applied forcings is analyzed. Finally, the seasonality of the internal variability of the circulation is explored as a possible mechanism to explain the seasonality of the responses. The primary results of these experiments are as follows: 1) There is a seasonal sensitivity in the circulation response to both the tropical and polar forcings. 2) The jet position response to each forcing is greatest in the transition seasons, and the jet speed response exhibits a seasonal sensitivity to both forcings, although the seasonal sensitivities are not the same. 3) The circulation response is nonlinear in the transition seasons, but approximately linear in the winter months. 4) The internal variability of the unforced circulation exhibits a seasonal sensitivity that may partly explain the seasonal sensitivity of the forced response. The seasonality of the internal variability of daily MERRA reanalysis data is compared to that of the model, demonstrating that the broad conclusions drawn from this idealized modeling study may be useful for understanding the jet response to anthropogenic forcing. [ABSTRACT FROM AUTHOR]

Recent work on atmospheric rivers (ARs) has led to a characterization of these impactful features as primarily cold-season phenomena. Here, an all-season analysis of AR incidence in the North Pacific basin is performed for the period spanning 1979-2014 using the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis dataset. An occurrence-based detection algorithm is developed and employed to identify and characterize ARs in instantaneous fields of anomalous vertically integrated water vapor transport. The all-season climatology and variability of AR frequencies due to the seasonal cycle, the El Niño-Southern Oscillation (ENSO), the Madden-Julian oscillation (MJO), and their interactions are presented based on composites of the detected features. The results highlight that ARs exist throughout the year over the North Pacific, although their preferred locations shift substantially throughout the year. This seasonal cycle manifests itself as northward and westward displacement of ARs during the Northern Hemisphere warm seasons, rather than an absolute change in the number of ARs within the domain. It is also shown that changes to the North Pacific mean-state due to ENSO and the MJO may enhance or completely offset the seasonal cycle of AR activity, but that such influences on AR frequencies vary greatly based on location. [ABSTRACT FROM AUTHOR]

A correction to an article on computer codes used in the wavebreaking analysis is presented.

Climate extremes such as drought and heat waves can cause substantial reductions in terrestrial carbon uptake. Advancing projections of the carbon uptake response to future climate extremes depends on (1) identifying mechanistic links between the carbon cycle and atmospheric drivers, (2) detecting and attributing uptake changes, and (3) evaluating models of land response and atmospheric forcing. Here, we combine model simulations, remote sensing products, and ground observations to investigate the impact of climate variability on carbon uptake in the Texas-northern Mexico region. Specifically, we (1) examine the relationship between drought, carbon uptake, and variability of El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) using the Joint UK Land-Environment Simulator (JULES) biosphere simulations from 1950-2012, (2) quantify changes in carbon uptake during record drought conditions in 2011, and (3) evaluate JULES carbon uptake and soil moisture in 2011 using observations from remote sensing and a network of flux towers in the region. Long-term simulations reveal systematic decreases in regional-scale carbon uptake during negative phases of ENSO and NAO, including amplified reductions of gross primary production (GPP) (−0.42 ± 0.18 Pg C yr−1) and net ecosystem production (NEP) (−0.14 ± 0.11 Pg C yr−1) during strong La Niña years. The 2011 megadrought caused some of the largest declines of GPP (−0.50 Pg C yr−1) and NEP (−0.23 Pg C yr−1) in our simulations. In 2011, consistent declines were found in observations, including high correlation of GPP and surface soil moisture ( r = 0.82 ± 0.23, p = 0.012) in remote sensing-based products. These results suggest a large-scale response of carbon uptake to ENSO and NAO, and highlight a need to improve model predictions of ENSO and NAO in order to improve predictions of future impacts on the carbon cycle and the associated feedbacks to climate change. [ABSTRACT FROM AUTHOR]

The Arctic lower atmosphere has warmed more rapidly than that of the globe as a whole, and this has been accompanied by unprecedented sea ice melt. Such large environmental changes are already having profound impacts on the flora, fauna, and inhabitants of the Arctic region. An open question, however, is whether these Arctic changes have an effect on the jet-stream and thereby influence weather patterns farther south. This broad question has recently received a lot of scientific and media attention, but conclusions appear contradictory rather than consensual. We argue that one point of confusion has arisen due to ambiguities in the exact question being posed. In this study, we frame our inquiries around three distinct questions: Can Arctic warming influence the midlatitude jet-stream? Has Arctic warming significantly influenced the midlatitude jet-stream? Will Arctic warming significantly influence the midlatitude jet-stream? We argue that framing the discussion around the three questions: Can it?, Has it?, and Will it? provides insight into the common themes emerging in the literature as well as highlights the challenges ahead. WIREs Clim Change 2015, 6:277-286. doi: 10.1002/wcc.337 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]