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Extreme maximum temperatures during Australian spring can have deleterious impacts on a range of sectors from health to wine grapes to planning for wildfires but are studied relatively little compared to spring rainfall. Spring maximum temperatures in Australia have been rising over recent decades, and it is important to understand how Australian spring maximum temperatures develop in the present and warming climate. Australia's climate is influenced by variability in the tropics and extratropics, but some of this influence impacts Australia differently from winter to summer and, consequently, may have different impacts on Australia as spring evolves. Using linear regression analysis, this paper explores the atmospheric dynamics and remote drivers of high maximum temperatures over the individual months of spring. We find that the drivers of early spring maximum temperatures in Australia are more closely related to low-level wind changes, which in turn are more related to the Southern Annular Mode than variability in the tropics. By late spring, Australia's maximum temperatures are proportionally more related to warming through subsidence than low-level wind changes and more closely related to tropical variability. This increased relationship with the tropical variability is linked with the breakdown of the subtropical jet through spring and an associated change in tropically forced Rossby wave teleconnections. An improved understanding of how the extratropics and tropics project onto the mechanisms that drive high maximum temperatures through spring may lead to improved sub-seasonal prediction of high temperatures in the future.

Interannual variability of the Southern Hemisphere subtropical jet (STJ) is assessed using atmospheric reanalyses during 1979–2018. The focus is on the austral winter season when the STJ is strongest and most distinct from the midlatitude eddy-driven jet (EDJ). Variations in the intensity and latitudinal position of the STJ are diagnosed using an index developed to discriminate between variations associated with the EDJ. STJ intensity and position variations are found to be tied to different mechanisms. An intensification of the STJ is associated with enhanced divergent outflow from diabatic heating over the equatorial Pacific Ocean, primarily resulting from eastern Pacific or canonical El Niño. This intensification is associated with a narrowing of the STJ and an in-place weakening of the EDJ. An equatorward-shifted STJ, however, appears to be eddy driven and is associated with an acceleration and poleward displacement of the EDJ, which projects onto the positive polarity of the southern annular mode. As has previously been reported, El Niño Modoki (or central Pacific El Niño) can act to shift the EDJ poleward during austral winter; thus, a possible pathway for changes in the position of the STJ is via tropically forced changes in the position of the EDJ. In contrast to previous studies, we also highlight a weakening and poleward shift of the STJ in association with an expansion of the Hadley circulation.

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services.

Australian maximum temperatures have reached record values in recent austral springs and are projected to increase further in a warming world. We focus on three record spring heat events in September 2013, October–November 2014 and October 2015, and examine the anomalous atmospheric circulation associated with these events in reanalysis and a sub-seasonal to seasonal prediction system, POAMA, to identify factors contributing to extreme heat over Australia. We find that an anomalous equivalent barotropic cyclonic circulation southwest of Australia and a quasi-stationary wave train formed by an upper-troposphere anticyclonic circulation over southern Australia and barotropic cyclone southeast of Australia are important features in these heat events, though the wave train was only observed in the latter two events. This wave train appears to be linked to the tropics, and particularly the tropical Indian Ocean, suggesting that teleconnections to the tropical Indian Ocean can be important for monthly spring extreme heat formation in Australia. However, the forecast relationship with the tropical Pacific Ocean was over-represented at the cost of the relationship between the Indian Ocean and upper-troposphere anomaly, limiting the ability of POAMA to forecast the full extent of the month- or 2 month-long heat extremes at zero lead time. This means that the model might underestimate the magnitude of future extreme heat events in spring, a factor that should be assessed in the next generation of seasonal forecast models.

Decadal climate prediction presumes there are decadal-timescale processes and mechanisms that, if initialized properly in models, potentially provide predictive skill more than one or two years into the future. Candidate mechanisms involve Pacific decadal variability and Atlantic multidecadal variability, elements of which involve slow fluctuations of tropical Pacific and Atlantic sea surface temperatures (SSTs) from positive anomalies (positive phase) to negative anomalies (negative phase). Here we use model experiments to show that there tends to be a weak opposite-sign SST response in the tropical Pacific when observed SSTs are specified in the Atlantic, while there is a weak same-sign SST response in the tropical Atlantic when observed SSTs are specified in the tropical Pacific. Net surface heat flux in the Atlantic and ocean dynamics in the Pacific play contrasting roles in the ocean response to specified SSTs in the respective basins. We propose that processes in the Pacific and Atlantic are sequentially interactive through the atmospheric Walker circulation along with contributions from midlatitude teleconnections for the Atlantic response to the Pacific. Atmospheric Walker circulation results in a two-way interaction between decadal-scale sea surface temperature variability in the Atlantic and Pacific, according to pacemaker climate modelling experiments.

The Southern Hemisphere summertime eddy-driven jet and storm tracks have shifted poleward over the recent few decades. In previous studies, explanations have mainly stressed the influence of external forcing in driving this trend. Here we examine the role of internal tropical SST variability in controlling the austral summer jet’s poleward migration, with a focus on interdecadal time scales. The role of external forcing and internal variability are isolated by using a hierarchy of Community Earth System Model version 1 (CESM1) simulations, including the pre-industrial control, large ensemble, and pacemaker runs. Model simulations suggest that in the early twenty-first century, both external forcing and internal tropical Pacific SST variability are important in driving a positive southern annular mode (SAM) phase and a poleward migration of the eddy-driven jet. Tropical Pacific SST variability, associated with the negative phase of the interdecadal Pacific oscillation (IPO), acts to shift the jet poleward over the southern Indian and southwestern Pacific Oceans and intensify the jet in the southeastern Pacific basin, while external forcing drives a significant poleward jet shift in the South Atlantic basin. In response to both external forcing and decadal Pacific SST variability, the transient eddy momentum flux convergence belt in the middle latitudes experiences a poleward migration due to the enhanced meridional temperature gradient, leading to a zonally symmetric southward migration of the eddy-driven jet. This mechanism distinguishes the influence of the IPO on the midlatitude circulation from the dynamical impact of ENSO, with the latter mainly promoting the subtropical wave-breaking critical latitude poleward and pushing the midlatitude jet to higher latitudes.

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

Extreme maximum temperatures during Australian spring can have deleterious impacts on a range of sectors from health to wine grapes to planning for wildfires, but are relatively understudied compared to spring rainfall. Spring maximum temperatures in Australia have been rising over recent decades, and, as such, it is important to understand how Australian spring maximum temperatures develop. Australia’s climate is influenced by variability in the tropics and extratropics, but some of this influence impacts Australia differently from winter to summer, and, consequently, may have different impacts on Australia as spring evolves. Using linear regression analysis, this paper explores the atmospheric dynamics and remote drivers of high maximum temperatures over the individual months of spring. We find that the drivers of early spring maximum temperatures in Australia are more closely related to low-level wind changes, which in turn are more related to the Southern Annular Mode than variability in the tropics. By late spring, Australia’s maximum temperatures are proportionally more related to warming through subsidence than low-level wind changes, and more closely related to tropical variability. This increased relationship with the tropical variability is linked with the breakdown of the subtropical jet through spring and an associated change in tropically-forced Rossby wave teleconnections. However, much of the maximum temperature variability cannot be explained by either tropical or extratropical variability. An improved understanding of how the extratropics and tropics projects onto the mechanisms that drive high maximum temperatures through spring may lead to improved sub-seasonal prediction of high temperatures in the future.

The occurrence of extreme hot and dry conditions in warm seasons can have large impacts on human health, energy and water supplies, agriculture and wildfires. Australian hot and dry extremes have been known to be associated with the occurrence of El Nino and other variations of tropospheric circulation. Here we identify an additional driver: variability of the stratospheric Antarctic polar vortex. On the basis of statistical analyses using observational data covering the past 40 yr, we show that weakenings and warmings of the stratospheric polar vortex, which episodically occur during austral spring, substantially increase the chances of hot and dry extremes and of associated fire-conducive weather across subtropical eastern Australia from austral spring to early summer. The promotion of these Australian climate extremes results from the downward coupling of the weakened polar vortex to tropospheric levels, where it is linked to the low-index polarity of the Southern Annular Mode, an equatorward shift of the mid-latitude westerly jet stream and subsidence and warming in the subtropics. Because of the long timescale of the polar vortex variations, the enhanced likelihood of early-summertime hot and dry extremes and wildfire risks across eastern Australia may be predictable a season in advance during years of vortex weakenings. Hot and dry climate extremes in Australia are linked to stratospheric polar vortex weakening, with potential implications for their predictability, according to statistical analyses of observational data from the past 40 years.

Studies have recently reported statistically significant relationships between observed year-to-year spring Antarctic ozone variability and the Southern Hemisphere annular mode and surface temperatures in spring–summer. This study investigates whether current chemistry–climate models (CCMs) can capture these relationships, in particular, the connection between November total column ozone (TCO) and Australian summer surface temperatures, where years with anomalously high TCO over the Antarctic polar cap tend to be followed by warmer summers. The interannual ozone–temperature teleconnection is examined over the historical period in the observations and simulations from the Whole Atmosphere Community Climate Model (WACCM) and nine other models participating in the Chemistry–Climate Model Initiative (CCMI). There is a systematic difference between the WACCM experiments forced with prescribed observed sea surface temperatures (SSTs) and those with an interactive ocean. Strong correlations between TCO and Australian temperatures are only obtained for the uncoupled experiment, suggesting that the SSTs could be important for driving both variations in Australian temperatures and the ozone hole, with no causal link between the two. Other CCMI models also tend to capture this relationship with more fidelity when driven by observed SSTs, although additional research and targeted modeling experiments are required to determine causality and further explore the role of model biases and observational uncertainty. The results indicate that CCMs can reproduce the relationship between spring ozone and summer Australian climate reported in observational studies, suggesting that incorporating ozone variability could improve seasonal predictions; however, more work is required to understand the difference between the coupled and uncoupled simulations.

Strong El Nino events have had significant impacts on society through their association with extreme events, such as droughts and floods. However, questions remain as to the robustness of strong El Nino events in forcing regional climate variability. The strong 1982, 1997 and 2015 El Nino events were of similar type and strength, but in eastern Australia they were associated with differing spring rainfall anomaly magnitudes and patterns. To understand these differences, we first determined the most important processes for teleconnecting the El Nino signal to east Australian spring rainfall using historical relationships with winds and sea level pressure. Then, using a 60-member atmospheric model ensemble, we estimated the influence of sea surface temperatures (SSTs) on Australian atmospheric circulation and rainfall during these three El Nino events relative to internal variability. We found that the different east Australian spring rainfall anomalies for each of the three El Nino events are best explained by differences in the strength of the meridional wind component of the regional circulation. All three El Nino events exhibited a positive sea level pressure anomaly to the south of Australia, which was associated with rainfall deficits along the southeast Australian coast. The experiments indicate the regional atmospheric circulation and rainfall differences were forced by SSTs during spring of 1982 and 1997, with their influence on the circulation in 2015 remaining unclear. We also show that SSTs adjacent to Australia further contributed to the modelled rainfall differences mainly by regulating moisture availability.

After exhibiting an upward trend since 1979, Antarctic sea ice extent (SIE) declined dramatically during austral spring 2016, reaching a record low by December 2016. Here we show that a combination of atmospheric and oceanic phenomena played primary roles for this decline. The anomalous atmospheric circulation was initially driven by record strength tropical convection over the Indian and western Pacific Oceans, which resulted in a wave-3 circulation pattern around Antarctica that acted to reduce SIE in the Indian Ocean, Ross and Bellingshausen Sea sectors. Subsequently, the polar stratospheric vortex weakened significantly, resulting in record weakening of the circumpolar surface westerlies that acted to decrease SIE in the Indian Ocean and Pacific Ocean sectors. These processes appear to reflect unusual internal atmosphere-ocean variability. However, the warming trend of the tropical Indian Ocean, which may partly stem from anthropogenic forcing, may have contributed to the severity of the 2016 SIE decline., Antarctic sea ice extent declined dramatically in austral spring 2016. This study shows the decline was initially driven by tropical convection resulting in a wave-3 circulation pattern, followed by weakened circumpolar surface westerlies initialised in the polar stratospheric vortex.

Recent work has suggested that tropical Pacific decadal variability and external forcings have had a comparable influence on the observed changes in the Southern Hemisphere (SH) summertime eddy-dri...

Simulations of 21st century climate with Community Earth System Model version 2 (CESM2) using the standard atmosphere (CAM6), denoted CESM2(CAM6), and the latest generation of the Whole Atmosphere Community Climate Model (WACCM6), denoted CESM2(WACCM6), are presented, and a survey of general results is described. The equilibrium climate sensitivity (ECS) of CESM2(CAM6) is 5.3°C, and CESM2(WACCM6) is 4.8°C, while the transient climate response (TCR) is 2.1°C in CESM2(CAM6) and 2.0°C in CESM2(WACCM6). Thus, these two CESM2 model versions have higher values of ECS than the previous generation of model, the CESM (CAM5) (hereafter CESM1), that had an ECS of 4.1°C, though the CESM2 versions have lower values of TCR compared to the CESM1 with a somewhat higher value of 2.3°C. All model versions produce credible simulations of the time evolution of historical global surface temperature. The higher ECS values for the CESM2 versions are reflected in higher values of global surface temperature increase by 2,100 in CESM2(CAM6) and CESM2(WACCM6) compared to CESM1 between comparable emission scenarios for the high forcing scenario. Future warming among CESM2 model versions and scenarios diverges around 2050. The larger values of TCR and ECS in CESM2(CAM6) compared to CESM1 are manifested by greater warming in the tropics. Associated with a higher climate sensitivity, for CESM2(CAM6) the first instance of an ice‐free Arctic in September occurs for all scenarios and ensemble members in the 2030–2050 time frame, but about a decade later in CESM2(WACCM6), occurring around 2040–2060.

A survey of intraseasonal, seasonal, and interannual precipitation and 850 hPa winds for various monsoon regimes around the world is presented for the Community Earth System Model Version 2 (CESM2) compared to observations and the previous generation CESM1. In CESM2 the south Asian monsoon has a reduction of excessive precipitation in the western Indian Ocean and an increase of precipitation in the eastern Bay of Bengal and land areas of Vietnam, Cambodia, and Laos. The seasonal timing of the south Asian monsoon, monsoon‐ENSO connections, and monsoon intraseasonal variability all are improved compared to CESM1. For the Australian monsoon, deficient precipitation over the Maritime Continent has been improved in CESM2 with increases of precipitation over the large tropical islands of Borneo, Celebes, and Papua New Guinea and decreases over southwestern Australia. In the West African monsoon, May–June seasonal rainfall occurs more preferentially over the African coast in CESM2 as in observations, and excessive rainfall over the Ethiopian region is reduced. During July–September in the West African monsoon, deficient precipitation over equatorial Africa in CESM1 has been lessened in CESM2, and there are increases in precipitation over the Guinean coast, though there is little overall improvement in the South African monsoon. In the South American monsoon, precipitation in CESM2 is improved with increased precipitation over the Amazon in central and western Brazil. CESM2 simulates a reduction of excessive precipitation seen in CESM1 over coastal Mexico extending up into the U.S. Great Plains in the North American monsoon.

Outputs from new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) promise improvement and enhancement of climate change projections information for Australia. Here we focus on three key aspects of CMIP6: what is new in these models, how the available CMIP6 models evaluate compared to CMIP5, and their projections of the future Australian climate compared to CMIP5 focussing on the highest emissions scenario. The CMIP6 ensemble has several new features of relevance to policymakers and others, for example, the integrated matrix of socioeconomic and concentration pathways. The CMIP6 models show incremental improvements in the simulation of the climate in the Australian region, including a reduced equatorial Pacific cold tongue bias, slightly improved rainfall teleconnections with large‐scale climate drivers, improved representation of atmosphere and ocean extreme heat events, as well as dynamic sea level. However, important regional biases remain, evident in the excessive rainfall over the Maritime Continent and rainfall pattern biases in the nearby tropical convergence zones. Projections of Australian temperature and rainfall from the available CMIP6 ensemble broadly agree with those from CMIP5, except for a group of CMIP6 models with higher climate sensitivity and greater warming and increase in some extremes after 2050. CMIP6 rainfall projections are similar to CMIP5, but the ensemble examined has a narrower range of rainfall change in austral summer in Northern Australia and austral winter in Southern Australia. Overall, future national projections are likely to be similar to previous versions but perhaps with some areas of improved confidence and clarity.

The Semiannual Oscillation (SAO) is a twice-yearly northward movement (in May-June-July, MJJ, and November-December-January, NDJ) of the circumpolar trough of sea level pressure (SLP) in the Southern Hemisphere with effects throughout the troposphere. During MJJ the second harmonic of SLP, describing the SAO, has low values of SLP north of 50°S in the subtropical South Pacific, while the first harmonic, which is dominant over the Australian sector, increases to its peak. This once-a-year peak in negative SLP gradients (decreasing to the east) between Australia and the ocean to its east extend to the equatorial Pacific. SO warm events since 1950, with an intensification of this seasonal cycle, have larger amplitude SST anomalies in the eastern equatorial Pacific in MJJ and during the following mature phase in NDJ. Weak amplification of the seasonal cycle in MJJ tends to be followed by larger-amplitude SST anomalies in the central equatorial Pacific during NDJ.

Background: Limited evidence is available regarding the association between heat exposure and morbidity in Brazil and how the effect of heat exposure on health outcomes may change over time. Objectives: This study sought to quantify the geographic, demographic and temporal variations in the heat–hospitalization association in Brazil from 2000–2015. Methods: Data on hospitalization and meteorological conditions were collected from 1,814 cities during the 2000–2015 hot seasons. Quasi-Poisson regression with constrained lag model was applied to examine city-specific estimates, which were then pooled at the regional and national levels using random-effect meta-analyses. Stratified analyses were performed by sex, 10 age groups, and 11 cause categories. Meta-regression was used to examine the temporal change in estimates of heat effect from 2000 to 2015. Results: For every 5°C increase in daily mean temperature during the 2000–2015 hot seasons, the estimated risk of hospitalization over lag 0–7 d rose by 4.0% [95% confidence interval (CI): 3.7%, 4.3%] nationwide. Estimated 6.2% [95% empirical CI (eCI): 3.3%, 9.1%] of hospitalizations were attributable to heat exposure, equating to 132 cases (95% eCI: 69%, 192%) per 100,000 residents. The attributable rate was greatest in children

A strengthening of the Amundsen Sea low from 1979 to 2013 has been shown to largely explain the observed increase in Antarctic sea ice concentration in the eastern Ross Sea and decrease in the Bellingshausen Sea. Here it is shown that while these changes are not generally seen in freely running coupled climate model simulations, they are reproduced in simulations of two independent coupled climate models: one constrained by observed sea surface temperature anomalies in the tropical Pacific and the other by observed surface wind stress in the tropics. This analysis confirms previous results and strengthens the conclusion that the phase change in the interdecadal Pacific oscillation from positive to negative over 1979–2013 contributed to the observed strengthening of the Amundsen Sea low and the associated pattern of Antarctic sea ice change during this period. New support for this conclusion is provided by simulated trends in spatial patterns of sea ice concentrations that are similar to those observed. These results highlight the importance of accounting for teleconnections from low to high latitudes in both model simulations and observations of Antarctic sea ice variability and change.

Australia experienced record high rainfall in austral spring 2010, which has previously been attributed to the concurrence of a strong La Nina event and a strong positive excursion of the Southern Annular Mode (SAM). In this study, we examine the role of the sea surface temperature (SST) trend over the recent 50 years, which has large warming over the tropical Indian, western Pacific and North Atlantic Oceans, in driving the extraordinary climate conditions of spring 2010, using the Australian Bureau of Meteorology coupled model seasonal forecast system. Four forecast sensitivity experiments were designed by using randomly chosen atmospheric initial conditions but with: (1) observed ocean initial conditions for 1 September 2010; (2) the same ocean initial conditions except the linear temperature trend over the period 1960–2010 was removed; (3) ocean initial conditions in which the trend was added to the climatological ocean state for 1 September; and (4) climatological ocean conditions only. A synergistic response to the La Nina SST anomalies and the SST trend was detected: the tropical rainfall anomalies were amplified over the western side of the Indo-Pacific warm-pool, which led to a significant increase of tropical upper tropospheric warming and a resultant increase of meridional temperature gradient in the Southern Hemisphere (SH) extratropics. Consequently, the SH eddy-driven jet was shifted poleward (i.e. positive phase of the SAM), which induced rainfall over subtropical Australia. Our findings highlight that the interaction of interannual anomalies and the trend may play an important role in the amplification of extreme events.

Given the Paris Agreement it is imperative there is greater understanding of the consequences of limiting global warming to the target 1.5° and 2°C levels above preindustrial conditions. It is challenging to quantify changes across a small increment of global warming, so a pattern-scaling approach may be considered. Here we investigate the validity of such an approach by comprehensively examining how well local temperatures and warming trends in a 1.5°C world predict local temperatures at global warming of 2°C. Ensembles of transient coupled climate simulations from multiple models under different scenarios were compared and individual model responses were analyzed. For many places, the multimodel forced response of seasonal-average temperatures is approximately linear with global warming between 1.5° and 2°C. However, individual model results vary and large contributions from nonlinear changes in unforced variability or the forced response cannot be ruled out. In some regions, such as East Asia, models simulate substantially greater warming than is expected from linear scaling. Examining East Asia during boreal summer, we find that increased warming in the simulated 2°C world relative to scaling up from 1.5°C is related to reduced anthropogenic aerosol emissions. Our findings suggest that, where forcings other than those due to greenhouse gas emissions change, the warming experienced in a 1.5°C world is a poor predictor for local climate at 2°C of global warming. In addition to the analysis of the linearity in the forced climate change signal, we find that natural variability remains a substantial contribution to uncertainty at these low-warming targets.

Data. Monthly maximum temperatures from the Australian Water Availability Project (AWAP) gridded dataset (Jones et al. 2009) were analyzed on a 0.25° grid over Australian land points. Observed sea surface temperatures from Hurrell et al. (2008) for 1981 and Reynolds et al. (2002) from 1982 onwards were used for the ENSO index (based on Nino-3.4 SSTs: 5°N–5°S, 170°–120°W) and the Indian Ocean dipole mode index [IOD: western pole (10°S–10°N, 50°–70°E); eastern pole (10°S–0°, 90°–110°E); Saji et al. 1999]. The SAM was calculated as the first EOF of mean sea level pressure (MSLP) anomalies over 20°–75°S (e.g., Lim et al. 2011) from the ERA-Interim reanalysis (Dee et al. 2011). Soil moisture estimates are from Raupach et al. (2009) for the upper-layer (

While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Early results demonstrate the substantial influence of internal climate variability on twentieth- to twenty-first-century climate trajectories. Global warming hiatus decades occur, similar to those recently observed. Internal climate variability alone can produce projection spread comparable to that in CMIP5. Scientists and stakeholders can use CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change.

Observed surface air temperatures over the contiguous U.S. for the second half of the twentieth century showed a slight cooling over the southeastern part of the country, the so-called “warming hole,” while temperatures over the rest of the country warmed. This pattern reversed after 2000. Climate model simulations show that the disappearance of the warming hole in the early 2000s is likely associated with the transition of the Interdecadal Pacific Oscillation (IPO) phase from positive to negative in the tropical Pacific in the late 1990s, coincident with the early 2000s slowdown of the warming trend in globally averaged surface air temperature. Analysis of a specified convective heating anomaly sensitivity experiment in an atmosphere-only model traces the disappearance of the warming hole to negative sea surface temperature anomalies and consequent negative precipitation and convective heating anomalies in the central equatorial Pacific Ocean associated with the negative phase of the IPO after 2000.

Multi-year (2-7 years) and decadal climate variability (MDCV) can have a profound influence on lives, livelihoods and economies. Consequently, learning more about the causes of this variability, the extent to whichit can be predicted, and the greater the clarity that we can provide on the climatic conditions that will unfold over coming years and decades is a high priority for the research community. This importance is reflected in new initiatives by WCRP, CLIVAR, and in the Decadal Climate Prediction Project (Boer et al., 2016) that target this area of research. Here we briefly examine some of the things we know, and have recently learnt, about the causes and predictability of Southern Hemisphere MDCV (SH MDCV), and current skill in its prediction. Fil: Power, Scott. Bureau Of Meteorology; Australia Fil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Chung, Christine. Bureau Of Meteorology; Australia Fil: Colman, Rob. Bureau Of Meteorology; Australia Fil: Kharin, Viatcheslav. Environment And Climate Change Canada; Canadá Fil: Boer, George. Environment And Climate Change Canada; Canadá Fil: Gergis, Joelle. University of Melbourne; Australia Fil: Henley, Benjamin. University of Melbourne; Australia Fil: McGregor, Shayne. Monash University. Faculty Of Science; Australia Fil: Arblaster, Julie. Monash University. Faculty Of Science; Australia Fil: Holbrook, Neil. University of Tasmania; Australia Fil: Liguori, Giovanni. Georgia Institute Of Technology; Estados Unidos

Accounting for natural decadal variability allows better prediction of short-term trends. This study looks at the ability of individual models, which are in phase with the Interdecadal Pacific Oscillation, to simulate the current global warming slowdown. The authors highlight that the current trend could have been predicted in the 1990s with this technique and the need for consistent hindcast skills to allow reliable decadal predictions.

We review the ideas behind the pattern scaling technique, and focus on its value and limitations given its use for impact assessment and within integrated assessment models. We present estimates of patterns for temperature and precipitation change from the latest transient simulations available from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), focusing on multi-model mean patterns, and characterizing the sources of variability of these patterns across models and scenarios. The patterns are compared to those obtained from the previous set of experiments, under CMIP3. We estimate the significance of the emerging differences between CMIP3 and CMIP5 results through a bootstrap exercise, while also taking into account the fundamental differences in scenario and model ensemble composition. All in all, the robustness of the geographical features in patterns of temperature and precipitation, when computed as multi-model means, is confirmed by this comparison. The intensity of the change (in both the warmer and cooler areas with respect to global temperature change, and the drier and wetter regions) is overall heightened per degree of global warming in the ensemble mean of the new simulations. The presence of stabilized scenarios in the new set of simulations allows investigation of the performance of the technique once the system has gotten close to equilibrium. Overall, the well established validity of the technique in approximating the forced signal of change under increasing concentrations of greenhouse gases is confirmed.

Future climate change projections for phase 5 of the Coupled Model Intercomparison Project (CMIP5) are presented for the Community Earth System Model version 1 that includes the Community Atmospheric Model version 5 [CESM1(CAM5)]. These results are compared to the Community Climate System Model, version 4 (CCSM4) and include simulations using the representative concentration pathway (RCP) mitigation scenarios, and extensions for those scenarios beyond 2100 to 2300. Equilibrium climate sensitivity of CESM1(CAM5) is 4.10°C, which is higher than the CCSM4 value of 3.20°C. The transient climate response is 2.33°C, compared to the CCSM4 value of 1.73°C. Thus, even though CESM1(CAM5) includes both the direct and indirect effects of aerosols (CCSM4 had only the direct effect), the overall climate system response including forcing and feedbacks is greater in CESM1(CAM5) compared to CCSM4. The Atlantic Ocean meridional overturning circulation (AMOC) in CESM1(CAM5) weakens considerably in the twenty-first century in all the RCP scenarios, and recovers more slowly in the lower forcing scenarios. The total aerosol optical depth (AOD) changes from ~0.12 in 2006 to ~0.10 in 2100, compared to a preindustrial 1850 value of 0.08, so there is less negative forcing (a net positive forcing) from that source during the twenty-first century. Consequently, the change from 2006 to 2100 in aerosol direct forcing in CESM1(CAM5) contributes to greater twenty-first century warming relative to CCSM4. There is greater Arctic warming and sea ice loss in CESM1(CAM5), with an ice-free summer Arctic occurring by about 2060 in RCP8.5 (2040s in September) as opposed to about 2100 in CCSM4 (2060s in September).

[1] A future Maunder Minimum type grand solar minimum, with total solar irradiance reduced by 0.25% over a 50 year period from 2020 to 2070, is imposed in a future climate change scenario experiment (RCP4.5) using, for the first time, a global coupled climate model that includes ozone chemistry and resolved stratospheric dynamics (Whole Atmosphere Community Climate Model). This model has been shown to simulate two amplifying mechanisms that produce regional signals of decadal climate variability comparable to observations, and thus is considered a credible tool to simulate the Sun's effects on Earth's climate. After the initial decrease of solar radiation in 2020, globally averaged surface air temperature cools relative to the reference simulation by up to several tenths of a degree Centigrade. By the end of the grand solar minimum in 2070, the warming nearly catches up to the reference simulation. Thus, a future grand solar minimum could slow down but not stop global warming.

In 2010 eastern Australia received its highest springtime (September–November) rainfall since 1900. Based on historical relationships with sea surface temperatures (SST) and other climate indices, this record rainfall in 2010 was shown to be largely commensurate with the occurrence of a very strong La Nina event and an extreme positive excursion of the SAM. The pattern and magnitude of the tropical SST anomalies in austral spring 2010 were diagnosed to be nearly perfect to produce high rainfall across eastern Australia. Key aspects of this SST pattern were the strong cold anomaly in the central equatorial Pacific, and the strong warm anomalies in the eastern Indian Ocean and the far western Pacific to the north of Australia. Although the recent upward trend in SSTs in the western Pacific/eastern Indian Ocean warm pool accounted for about 50 % of the SST anomaly surrounding northern Australia in 2010, the contribution by the warming trend in these SSTs to the Australian rainfall anomaly in 2010 was assessed to be relatively modest. The strong positive swing in SAM was estimated to have accounted for upwards of 40 % of the regional anomaly along the central east coastal region and about 10 % of the area mean anomaly across eastern Australia. This contribution by the SAM suggests that a significant portion of the rainfall in 2010 may not have been seasonally predictable. However, predictability arising from the promotion of high SAM by the extreme La Nina event can not be ruled out.

A modelling study shows that cutting greenhouse-gas emissions has the potential to stabilize global temperature increases, but predicts that sea level will continue to rise for centuries, and rapidly so, unless aggressive mitigation measures are set in place. There is a common perception that, if human societies make the significant adjustments necessary to substantively cut emissions of greenhouse gases, global temperature increases could be stabilized, and the most dangerous consequences of climate change could be avoided. Here we show results from global coupled climate model simulations with the new representative concentration pathway mitigation scenarios to 2300 to illustrate that, with aggressive mitigation in two of the scenarios, globally averaged temperature increase indeed could be stabilized either below 2 °C or near 3 °C above pre-industrial values. However, even as temperatures stabilize, sea level would continue to rise. With little mitigation, future sea-level rise would be large and continue unabated for centuries. Though sea-level rise cannot be stopped for at least the next several hundred years, with aggressive mitigation it can be slowed down, and this would buy time for adaptation measures to be adopted.

A linear trend calculated for observed annual mean surface air temperatures over the United States for the second-half of the twentieth century shows a slight cooling over the southeastern part of the country, the so-called warming hole, while temperatures over the rest of the country rose significantly. This east–west gradient of average temperature change has contributed to the observed pattern of changes of record temperatures as given by the ratio of daily record high temperatures to record low temperatures with a comparable east–west gradient. Ensemble averages of twentieth-century climate simulations in the Community Climate System Model, version 3 (CCSM3), show a slight west–east warming gradient but no warming hole. A warming hole appears in only several ensemble members in the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset and in one ensemble member of simulated twentieth-century climate in CCSM3. In this model the warming hole is produced mostly from internal decadal time-scale variability originating mainly from the equatorial central Pacific associated with the Interdecadal Pacific Oscillation (IPO). Analyses of a long control run of the coupled model, and specified convective heating anomaly experiments in the atmosphere-only version of the model, trace the forcing of the warming hole to positive convective heating anomalies in the central equatorial Pacific Ocean near the date line. Cold-air advection into the southeastern United States in winter, and low-level moisture convergence in that region in summer, contribute most to the warming hole in those seasons. Projections show a disappearance of the warming hole, but ongoing greater surface temperature increases in the western United States compared to the eastern United States.

The simulation characteristics of the Asian–Australian monsoon are documented for the Community Climate System Model, version 4 (CCSM4). This is the first part of a two part series examining monsoon regimes in the global tropics in the CCSM4. Comparisons are made to an Atmospheric Model Intercomparison Project (AMIP) simulation of the atmospheric component in CCSM4 [Community Atmosphere Model, version 4, (CAM4)] to deduce differences in the monsoon simulations run with observed sea surface temperatures (SSTs) and with ocean–atmosphere coupling. These simulations are also compared to a previous version of the model (CCSM3) to evaluate progress. In general, monsoon rainfall is too heavy in the uncoupled AMIP run with CAM4, and monsoon rainfall amounts are generally better simulated with ocean coupling in CCSM4. Most aspects of the Asian–Australian monsoon simulations are improved in CCSM4 compared to CCSM3. There is a reduction of the systematic error of rainfall over the tropical Indian Ocean for the South Asian monsoon, and well-simulated connections between SSTs in the Bay of Bengal and regional South Asian monsoon precipitation. The pattern of rainfall in the Australian monsoon is closer to observations in part because of contributions from the improvements of the Indonesian Throughflow and diapycnal diffusion in CCSM4. Intraseasonal variability of the Asian–Australian monsoon is much improved in CCSM4 compared to CCSM3 both in terms of eastward and northward propagation characteristics, though it is still somewhat weaker than observed. An improved simulation of El Niño in CCSM4 contributes to more realistic connections between the Asian–Australian monsoon and El Niño–Southern Oscillation (ENSO), though there is considerable decadal and century time scale variability of the strength of the monsoon–ENSO connection.

This is the second part of a two part series studying simulation characteristics of the Community Climate System Model, version 4 (CCSM4) for various monsoon regimes around the global tropics. Here, the West African, East African, North American, and South American monsoons are documented in CCSM4. Comparisons are made to an Atmospheric Model Intercomparison Project (AMIP) simulation of the atmospheric component in CCSM4 (CAM4), to deduce differences in the monsoon simulations run with observed SSTs and with ocean–atmosphere coupling. These simulations are also compared to a previous version of the coupled model (CCSM3) to evaluate progress. In most, but not all instances, monsoon rainfall is too heavy in the uncoupled AMIP run with the Community Atmosphere Model, version 4 (CAM4), and monsoon rainfall amounts are generally better simulated with ocean coupling in CCSM4. Some aspects of the monsoon simulations are improved in CCSM4 compared to CCSM3. Early-season rainfall in the West African monsoon is better simulated in CAM4 than in CCSM4 presumably because of the specification of SSTs in the Gulf of Guinea, but the Sahel rainfall season is captured better in CCSM4 as are the African easterly jet and the tropical easterly jet. Improvements in the simulation of the Sahel rainy season (July, August, and September) in CCSM4 compared with CCSM3 are significant, but problems remain in the simulation of the early season (May and June) in association with the misrepresentation of eastern Atlantic (Gulf of Guinea) SSTs. Precipitation distributions and the southwesterly low-level inflow in the North American monsoon are improved in CCSM4 compared to CCSM3. Both CAM4 and CCSM4 reproduce the seasonal evolution of rainfall over the South American monsoon region, but the location of maximum rainfall is misplaced to the northeast in both models.