Large Ensemble Climate Research Articles

Understanding the recent increase in multiyear La Niñas

Five out of six La Niña events since 1998 have lasted two to three years. Why so many long-lasting multiyear La Niña events have emerged recently and whether they will become more common remains unknown. Here we show that ten multiyear La Niña events over the past century had an accelerated trend, with eight of these occurring after 1970. The two types of multiyear La Niña events over this time period followed either a super El Niño or a central Pacific El Niño. We find that multiyear La Niña events differ from single-year La Niñas by a prominent onset rate, which is rooted in the western Pacific warming-enhanced zonal advective feedback for the central Pacific multiyear La Niña events type and thermocline feedback for the super El Niño multiyear La Niña events type. The results from large ensemble climate simulations support the observed multiyear La Niña events–western Pacific warming link. More multiyear La Niña events will exacerbate adverse socioeconomic impacts if the western Pacific continues to warm relative to the central Pacific.

Assessing the impact of climate change on sediment discharge using a large ensemble rainfall dataset in Pekerebetsu River basin, Hokkaido

Increased rainfall associated with climate change can increase sediment discharge. The supply of fine sediment from slope failures inhibits bed armoring of mountain rivers and increases sediment discharge to the downstream reaches. Floods without slope failures lead to bed erosion and armoring and may ultimately decrease sediment discharge. Thus, it is important to consider sediment discharge from slope failure and bed erosion as factors affecting sediment production. Climate change affects not only the rainfall amount, but also the temporal rainfall pattern; consequently, the pattern affects the sediment production factors and the amount of sediment discharge. However, changes in sediment discharge due to climate change based on sediment production sources have not yet been clarified. In this study, we statistically analyzed 1200 results simulated using a physics-based sediment runoff model to assess the impact of changes in temporal rainfall patterns on sediment discharge and sediment production sources in the Pekerebetsu River Basin. In the simulations, we used the rainfall predicted in d4PDF (Database for policy decision-making for future climate change), a large ensemble climate simulation database at 5 km and 20 km resolutions. Our results showed that the climate-driven increase in sediment discharge was considerably larger than that of rainfall. An increase in short-term heavy rainfall increased the supply of fine sediments from slope failure. This resulted in the suppression of bed armoring and a large increase in sediment discharge. Thus, the increase in sediment discharge is not only caused by an increase in rainfall but also by changes in temporal rainfall patterns and sediment production factors. The sediment discharge calculated for the 20 km resolution climate projection was nearly one order of magnitude smaller than that for the 5 km resolution. This suggests that the 20 km resolution climate projections do not adequately represent orographic rainfall in the mountains and thus, do not adequately reproduce extreme sediment discharge events. An increased sediment supply causes bed aggradation and decreases the river conveyance capacity of the downstream channel. The model developed in this study will contribute to flood risk analysis and flood control planning for increased rainfall due to climate change.

Assessing the impacts of climate change and dam development on potential flood hazard and damages in the Cambodian floodplain of the lower mekong basin

Study RegionMekong River Basin Study focusClimate change has become a global environmental and socio-economical issue potentially affecting river hydrology and downstream flood characteristics. Climate change and upstream dam construction are the two main driving factors in altering the hydrology in the Mekong River Basin. Cambodian floodplain is located in the vulnerable flood zone to be affected by climate change and upstream dam development. This study investigated the potential impacts of future climate change and dam construction on flood hazards and agricultural damages in the Cambodian floodplain of the Mekong River. Climate outputs from MRI-AGCM3.2S under different sea surface temperature (SST) scenarios and a large ensemble climate dataset (d4PDF) under 4 K increasing scenario were used as input to a fully distributed rainfall-runoff-inundation (RRI) model for future projection of flood in the Lower Mekong Basin. New hydrological insights for the regionThe results indicated that the changes in extreme flood events (25-year flood) showed an increase of agricultural flood damages under climate change impact by 18–28% under different SST scenarios. These increases would be only 5–13% under integrated impacts from future climate change and dam construction. Under the 4 K increasing scenario, the agricultural damages for extreme flood events of 10-year, 50-year, and 100-year return periods would increase by 32%, 38%, and 39% for climate change impact alone and 17%, 29%, and 31% for combined climate change and dam development scenario, respectively. The climate change impacts would have more influential on increasing the magnitude of extreme floods than the reduction caused by dam reservoir constructions in the future projection in the Cambodian floodplain of the Mekong River Basin. The impact of dam constructions tends to have greater impact on reducing smaller extreme events; however, their impacts are relatively smaller for larger extreme flood events (i.e., 100-year flood).

Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change

Abstract. Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer “what if” questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost 2 ∘C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least 2.9 ∘C accounts for 60 %–80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.

Avoidable heat risk under scenarios of carbon neutrality by mid-century

To prevent anthropogenic warming of the climate system above dangerous thresholds, governments are required by the Paris Agreement to peak global anthropogenic CO2 emissions and to reach a net zero CO2 emissions level (also known as carbon neutrality). Growing concerns are being expressed about the increasing heat stress caused by the interaction of changes in temperature and humidity in the context of global warming. Although much effort has been made to examine future changes in heat stress and associated risks, gaps remain in understanding the quantitative benefits of heat-risk avoidance from carbon-neutral policies, limited by the traditional climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Here we quantify the avoided heat risk during 2040–2049 under two scenarios of global carbon neutrality by 2060 and 2050, i.e., moderate green (MODGREEN) and strong green (STRGREEN) recovery scenarios, relative to the baseline scenario (FOSSIL), based on multi-model large ensemble climate projections from a new climate model intercomparison project (CovidMIP) that endorsed by CMIP6. We show that global population exposure to extreme heat stress increases by approximately four times its current level during 2040–2049 under the FOSSIL scenario, whereas the heat exposure could be reduced by as much as 12 % and 23 % under the MODGREEN and STRGREEN scenarios, respectively. Moreover, global mean heat-related mortality risk is mitigated by 14 % (24 %) under the MODGREEN (STRGREEN) scenario during 2040–2049 relative to the FOSSIL scenario. Additionally, the aggravating heat risk could be mitigated by around a tenth by achieving carbon neutrality 10 years earlier (2050 versus 2060). In terms of spatial pattern, this heat-risk avoidance from low-carbon policies is typically greater in low-income countries. Our findings assist governments in advancing early climate change mitigation policy-making.

Risk-based versus storyline approaches for global warming impact assessment on basin-averaged extreme rainfall: a case study for Typhoon Hagibis in eastern Japan

Two methods exist to address the degree to which past extreme events and associated disasters will be intensified due to climate change: storyline approaches and risk-based approaches. However, the risk-based approach applied to weather similar to the target event (typhoons, a stationary weather front,…etc) becomes theoretically similar to the storyline approach. We examine this theory for the climate change impact of a real event, Typhoon Hagibis, which caused devastating flood damage to eastern Japan in 2019, while focusing on basin-averaged accumulated rainfall (BAAR) in major eastern river basins. A risk-based approach was conducted to determine the future change of BAAR by calculating the quantile change corresponding to Hagibis from the probability distribution of typhoon-induced events in a large ensemble climate simulation dataset database for Policy Decision-making for Future climate change (past, +2K and +4K future climates). A storyline approach for Typhoon Hagibis was realized using a pseudo global warming (PGW) experiment with a 5 km non-hydrostatic model. The projected BAAR in the two approaches were consistent for all target basins, supporting the robustness of the calculated changes in extreme catchment precipitation. This presents an important practical benefit: one can assess future climate change impact on a past symbolic event using either PGW experiments or large ensemble climate projections for the target weather.

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

AbstractThere is currently large uncertainty over the impacts of climate change on precipitation trends over the US west coast. Atmospheric rivers (ARs) are a significant source of US west coast precipitation and trends in ARs can provide insight into future precipitation trends. There are already a variety of different methods used to identify ARs, but many are used in contexts that are often difficult to apply to large climate datasets due to their computational cost and requirement of integrated vapor transport as an input variable, which can be expensive to compute in climate models at high temporal frequencies. Using deep learning (DL) to track ARs is a unique approach that can alleviate some of the computational challenges that exist in more traditional methods. However, some questions still remain regarding its flexibility and robustness. This research investigates the consistency of a DL methodology of tracking ARs with more established algorithms to demonstrate its high‐level performance for future studies.

Forecast‐ready models to support fisheries' adaptation to global variability and change

AbstractOcean and climate drivers affect the distribution and abundance of marine life on a global scale. Marine ecological forecasting seeks to predict how living marine resources respond to physical variability and change, enabling proactive decision‐making to support climate adaptation. However, the skill of ecological forecasts is constrained by the skill of underlying models of both ocean state and species‐environment relationships. As a test of the skill of data‐driven forecasts for fisheries, we developed predictive models of catch‐per‐unit‐effort (CPUE) of tuna and billfish across the south‐west Pacific Ocean, using a 12‐year time series of catch data and a large ensemble climate reanalysis. Descriptors of water column structure, particularly temperature at depth and upper ocean heat content, emerged as useful predictors of CPUE across species. Enhancing forecast skill over sub‐seasonal to multi‐year timescales in any system is likely to require the inclusion of sub‐surface ocean data and explicit consideration of regional physical dynamics.

Where are the coexisting parallel climates? Large ensemble climate projections from the point of view of chaos theory.

We review the recent results of large ensemble climate projections considering them to be the simulations of chaotic systems. The quick spread of an initially localized ensemble in the first weeks after initialization is an appearance of the butterfly effect, illustrating the unpredictability of the dynamics. We show that the growth rate of uncertainty (an analog of the Lyapunov exponent) can be determined right after initialization. The next phase corresponds to a convergence of the no longer localized ensemble to the time-dependent climate attractor and requires a much longer time. After convergence takes place, the ensemble faithfully represents the climate dynamics. Concerning a credible simulation, the observed signal should then wander within the spread of the converged ensemble all the time, i.e., to behave just as any of the ensemble members. As a manifestation of the chaotic-like climate dynamics, one can imagine that beyond the single, observed time-dependent climate, a plethora of parallel climate realizations exists. Converged climate ensembles also define the probability distribution by which the physical quantities of the different climate realizations occur. Large ensemble simulations were shown earlier to be credible in the sense formulated. Here, in addition, an extended credibility condition is given, which requires the ensemble to be a converged ensemble, valid also for low-dimensional models. Interestingly, to the best of our knowledge, no low-order physical or engineering systems subjected to time-dependent forcings are known for which a comparison between simulation and experiment would be available. As illustrative examples, the CESM1-LE climate model and a chaotic pendulum are taken.

Future Changes in Tropical and Extratropical Cyclones Affecting Hokkaido and Their Related Precipitation Based on Large-Ensemble Climate Simulations

Abstract This study investigates the impact of future climate warming on tropical cyclones (TC) and extratropical cyclones (ETC) using the database for Policy Decision-Making for Future Climate Change (d4PDF) large ensemble simulations. Cyclone tracking was performed using the neighbor enclosed area tracking algorithm (NEAT), and TC and ETCs were identified over the western North Pacific Ocean (WNP). For cyclone frequency, it was revealed that, although a slight underestimation of the total number of TCs and ETCs in both the WNP and near Hokkaido, Japan, exists, the d4PDF reproduced the spatial distribution of both TC and ETC tracks well when compared with observations/reanalysis. The 4-K warming scenarios derived from six different sea surface temperature warming patterns showed robust decreases in TC frequency in the tropical WNP and a slight reduction in ETCs near Japan. Next, precipitation characteristics for TCs or ETCs in the vicinity of Hokkaido were examined using 5-km-mesh regional climate ensemble simulations. Four representative cyclone locations near Hokkaido are identified using K-means clustering and revealed distinct precipitation characteristics between clusters, with higher TC-associated precipitation than ETC-associated precipitation and the heaviest precipitation in the southern portion of the prefecture. The 4-K warming scenarios revealed increased precipitation for all cyclone placements for both TCs and ETCs. Last, average cyclone intensity, translation speed, and size were examined. It was shown that TCs in future climates are more intense, propagate more slowly, and are smaller in terms of enclosed vorticity area as they approach Hokkaido. For ETCs, mean intensity does not change much; they travel slightly faster, and become smaller.

Assessment of the changing role of lower tropospheric temperature advection under arctic amplification using a large ensemble climate simulation dataset

The role of temperature advection in the Arctic lower troposphere under changing level of global warming is investigated using a large-ensemble climate simulation dataset. Taking the 30-year climatology of the non-warming simulation (HPB-NAT) as a reference, we examined the difference in temperature advection under changing basic states of the historical experiment (HPB) and 2 K and 4 K warming experiments (HFB-2K and HFB-4K) and decomposed them into terms related to dynamical changes, thermodynamical changes and the eddy term which is a covariance term related to the effect of sub-monthly transient eddies. Under the HPB experiment, it was found that the total change in advection hangs in a balance between the positive signal located along the sea-ice boundary in the North Atlantic and along the Eurasian continent driven by a stronger dynamical term and a negative signal in the thermodynamical term and eddy term. It is found that with the progression of global warming the dynamical term of advection increases due to changes in the large-scale atmospheric circulation, but the thermodynamical term and eddy term decrease due to weaker temperature gradient and increased sensible heat flux from the newly opened ice-free ocean, respectively. Atmospheric temperature advection terms related to large-scale atmospheric circulation partially cancels one another, and the relative importance of the eddy term diverging locally induced sensible heat from the newly opened ice-free ocean dominates as global warming progresses.

Enhancement of river flooding due to global warming

Human-induced climate change has increased the frequency and intensity of heavy precipitation1. Due to the complexity of runoff generation and the streamflow process, the historical impact of human-induced climate change on river flooding remains uncertain. Here, we address the question of whether anthropogenic climate change has altered the probability of the extreme river flood events for the period 1951–2010 based on simulated river discharge derived from large ensemble climate experiments with and without human-induced climate change. The results indicate that human-induced climate change altered the probabilities of 20 of the 52 analyzed flood events. Fourteen of these 20 flood events, which occurred mainly in Asia and South America, were very likely to have been enhanced by human-induced climate change due to an increase in heavy precipitation. Conversely, two flood events in North/South America and two flood events in Asia and two flood events in Europe were suppressed by human-induced climate change, perhaps as a result of lower snowfall. Human-induced climate change has enhanced flooding more prominently in recent years, providing important insights into potential adaptation strategies for river flooding.

Large-ensemble climate simulations to assess changes in snow stability over northern Japan

AbstractTo examine the influence of global warming including increased heavy snowfall frequency on the potential of natural dry snow avalanche frequency and magnitude, we estimated the frequency of weak layer formation and the associated slab overload above the weak layer over northern Japan. The estimation was numerically performed using climate models' output for 1800 winter simulations in each of the historical (1951–2010) and +4°C experiments by forcing a physical-based snowpack model with the result of the climate models. Here the +4°C experiment was defined as a climate when the global mean air temperature had increased by 4°C from the preindustrial level. The estimation results showed that the probability of weak layer formation, identified by the natural stability index, would decrease all over the area because of the shorter age of the weak layers caused by a warmer climate, indirectly indicating a potential decrease in avalanche frequency. However, because of increased heavy snowfall frequency, slab overload would increase by 10–15% in inland areas for weak layers of decomposing fragments/precipitation particles and the mountainous area facing the Sea of Japan for weak layers of facets/depth hoar, thereby potentially indicating an increased magnitude of avalanches.

Future changes in extreme storm surge based on a maximum potential storm surge model for East Asia

ABSTRACT We analyzed tropical cyclones (TC) based on the theory of Maximum Potential Intensity (MPI) and Maximum Potential Surge (MPS) for a long-term assessment of extreme TC intensity and storm surge heights. We investigated future changes in the MPI fields and MPS for different global warming levels based on 150-year continuous scenario projections (HighResMIP) and large ensemble climate projections (d4PDF/d2PDF). Focusing on the Western North Pacific Ocean (WNP), we analyzed future changes in the MPI and found that it reached a maximum in the latitudinal range of 30–40°N in September. We also analyzed future changes in the MPS in major bays of East Asia and along the Pacific coast of Japan. Future changes in the MPS were projected, and it was confirmed that changes in the MPS are larger in bays where large storm surge events have occurred in the past.

Regional Differences in Summertime Extremely High Temperature in Japan due to Global Warming

Abstract Knowledge of regional differences in future climate projections is important for effective adaptation strategies. Extreme events often arise regionally, but multiscale factors likely act together. Hence, we need discussion of multiple scales for the regional characteristics of future changes of extremes. In this study, using a large ensemble climate simulation database (d4PDF) created by global and regional climate models, the change in the temperature extreme defined as the top 10% of summertime daily maximum temperature in Japan is investigated under a globally 2- and 4-K-warmer climate, with emphasis on its regionality. Under global warming, the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A simple composite analysis of extreme events shows that the high temperature occurs under a site-specific spatial pattern of sea level pressure (SLP), with a common feature of a warm anomaly up to the upper troposphere over the sites. The SLP pattern reflects the local topography and favors a foehnlike wind that increases the near-surface temperature. The impact of climate change in SLP on the foehn-inducing pattern varies with site, leading to regional differences in high-temperature changes. Therefore, the dynamic response of SLP to global warming results in a characteristic spatial distribution for the high-temperature change, which differs from the distribution for the mean-temperature change that generally shows the thermodynamic response. The characteristic is expected to appear in mountainous regions of the world, and this study helps in understanding future projections of high temperature there. Significance Statement Regionality in future climate projections strongly influences the usefulness of adaptation strategies to climate change. This study indicates that the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A site-specific spatial pattern of sea level pressure (SLP) reflecting the local topography contributes to the location of high temperature via a foehnlike wind. The impact of climate change in SLP on the pattern varies with site and leads to the regionality in high-temperature changes, which is the dynamic response to global warming unlike the thermodynamic response appearing on the mean-temperature change. This study helps us to understand future projections of temperature extreme in mountainous regions and their surroundings around the world.

Can signal-to-noise ratio indicate prediction skill? Based on skill assessment of 1-month lead prediction of monthly temperature anomaly over Japan

We present a skill assessment of 1-month lead deterministic predictions of monthly surface air temperature anomalies over most part of Japan based on a large-ensemble climate model, SINTEX-F. We found that September is the most predictable and the only month in which the prediction skill beats the persistence. Interestingly, however, prediction of December becomes skillful (correlation skill: 0.67) when we select only years in which the signal-to-noise ratio of the predictions is relatively high. This means that the signal-to-noise ratio can partly indicate the prediction skill. The inter-member co-variability suggests that a combination of the tropical Pacific and western Indian Ocean surface temperature is the key for the prediction. Although seasonal climate prediction in the mid-latitude regions, such as Japan, is still challenging in general, providing the signal-to-noise ratio and the inter-member co-variability in addition to the real-time prediction might be useful for stakeholders to know how confident the individual prediction is, as well as its potential sources of predictability. Such information can be helpful to take necessary mitigation measures to reduce socio-economic losses associated with extreme climate.

Large increases of multi-year droughts in north-western Europe in a warmer climate

Three consecutive dry summers in western Europe (2018–2019–2020) had widespread negative impacts on society and ecosystems, and started societal debate on (changing) drought vulnerability and adaptation measures. We investigate the occurrence of multi-year droughts in the Rhine basin, with a focus on event probability in the present and in future warmer climates. Additionally, we investigate the temporally compounding physical drivers of multi-year drought events. A combination of multiple reanalysis datasets and multi-model large ensemble climate model simulations was used to provide a robust analysis of the statistics and physical processes of these rare events. We identify two types of multi-year drought events (consecutive meteorological summer droughts and long-duration hydrological droughts), and show that these occur on average about twice in a 30 year period in the present climate, though natural variability is large (zero to five events can occur in a single 30 year period). Projected decreases in summer precipitation and increases in atmospheric evaporative demand, lead to a doubling of event probability at 1 ^circC additional global warming relative to present-day and an increase in the average length of events. Consecutive meteorological summer droughts are forced by two, seemingly independent, summers of lower than normal precipitation and higher than normal evaporative demand. The soil moisture response to this temporally compound meteorological forcing has a clear multi-year imprint, resulting in a relatively larger reduction of soil moisture content in the second year of drought, and potentially more severe drought impacts. Long-duration hydrological droughts start with a severe summer drought followed by lingering meteorologically dry conditions. This limits and slows down the hydrological recovery of soil moisture content, leading to long-lasting drought conditions. This initial exploration provides avenues for further investigation of multi-year drought hazard and vulnerability in the region, which is advised given the projected trends and vulnerability of society and ecosystems.

Precipitation and Temperature Climatologies over India: A Study with AGCM Large Ensemble Climate Simulations

This study investigated the precipitation and temperature climatologies over India from large ensemble (100 members) historical climate simulations in two recent past climate periods (1951–1980 and 1981–2010). The main focus was to statistically examine the usefulness of such large historical climate simulations by discussing (1) the precipitation and temperature climatologies and their distribution patterns, (2) the annual cycle of the temperature and precipitation climatologies, and (3) the frequency distributions and potential spatial patterns of climate variability. We applied empirical orthogonal function to understand the characteristics and normal probability distribution function to investigate the frequency. Results indicated good agreements of these large ensembles simulated results with the observation over Indian region. The precipitation amount over many regions of India is decreased and temperature over entire India is increased in 1981–2010 compared to that in 1951–1980. The annual cycle of the precipitations over India indicated a decrease of the precipitation amounts from June through October, while an increase of precipitation for the months from November through January. The annual cycle of the temperature over India indicated an increase of temperature during July through March. The frequency distributions of monthly precipitations and temperatures indicate an overall decrease of precipitation and an overall increase of temperature in recent climate period. The reason of decreased precipitation in recent climate period is attributed to a decrease of relative humidity and cloud together with weaker vertical velocity over Indian region during 1981–2010. Overall study validates the usefulness of these large ensemble climate simulations for the assessment of climate over India and suggests that these datasets may be used for various purposes related to weather and climate over India.

Interpreting extreme climate impacts from large ensemble simulations—are they unseen or unrealistic?

Large-ensemble climate model simulations can provide deeper understanding of the characteristics and causes of extreme events than historical observations, due to their larger sample size. However, adequate evaluation of simulated ‘unseen’ events that are more extreme than those seen in historical records is complicated by observational uncertainties and natural variability. Consequently, conventional evaluation and correction methods cannot determine whether simulations outside observed variability are correct for the right physical reasons. Here, we introduce a three-step procedure to assess the realism of simulated extreme events based on the model properties (step 1), statistical features (step 2), and physical credibility of the extreme events (step 3). We illustrate these steps for a 2000 year Amazon monthly flood ensemble simulated by the global climate model EC-Earth and global hydrological model PCR-GLOBWB. EC-Earth and PCR-GLOBWB are adequate for large-scale catchments like the Amazon, and have simulated ‘unseen’ monthly floods far outside observed variability. We find that the realism of these simulations cannot be statistically explained. For example, there could be legitimate discrepancies between simulations and observations resulting from infrequent temporal compounding of multiple flood peaks, rarely seen in observations. Physical credibility checks are crucial to assessing their realism and show that the unseen Amazon monthly floods were generated by an unrealistic bias correction of precipitation. We conclude that there is high sensitivity of simulations outside observed variability to the bias correction method, and that physical credibility checks are crucial to understanding what is driving the simulated extreme events. Understanding the driving mechanisms of unseen events may guide future research by uncovering key climate model deficiencies. They may also play a vital role in helping decision makers to anticipate unseen impacts by detecting plausible drivers.

No Internal Connections Detected Between Low Frequency Climate Modes in North Atlantic and North Pacific Basins

AbstractPrevious studies documented possible connections between low frequency climate modes in the Northern Hemisphere ocean basins. We use observed sea surface temperatures and 270 large ensemble climate model simulations, which allows for improved methods of separating external and internal variability, such as removing the ensemble mean from each simulation. Detrending methods for observations have also improved since some of these previous studies were conducted. We also devise a modified statistical test using bootstrapping that is tuned specifically to this analysis. With these tools, we reexamine relationships among these modes. While previous studies have argued for the existence of an inter‐basin link, our results suggest that any internal connections between these modes are indistinguishable from random noise. Further, we show that external forcing affects each region in similar ways. This suggests that anthropogenic warming can cause an indirect link between the two basins, confounding the interpretation of a potential relationship.