Search

An apparent increase in observed cold extremes over recent decades in the northern midlatitudes has been reported, in contrast to robust decreases predicted by climate models. This discrepancy has led to suggestions that models fail to accurately simulate changes in weather patterns caused by Arctic warming. Here, we show that the observed frequency and intensity of midlatitude cold extremes have strongly decreased since 1990 and are consistent with modeled trends. The previously reported increase in cold extremes was overestimated due to an artifact of changing data coverage. We also show that the fraction of land with observed cold extreme increases over recent decades is consistent with model internal variability on top of a near-uniform forced reduction in cold extremes across the midlatitudes. Our results provide strong evidence of a decrease in midlatitude cold extremes over recent decades and consistency between models and observations. [ABSTRACT FROM AUTHOR]

We analyze the sources for spread in the response of the Northern Hemisphere wintertime stratospheric polar vortex (SPV) to global warming in Climate Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) model projections. About half of the intermodel spread in SPV projections by CMIP6 models, but less than a third in CMIP5 models, can be attributed to the intermodel spread in stationary planetary wave driving. In CMIP6, SPV weakening is mostly driven by increased upward wave flux from the troposphere, while SPV strengthening is associated with increased equatorward wave propagation away from the polar stratosphere. We test hypothesized factors contributing to changes in the upward and equatorward planetary wave fluxes and show that an across‐model regression using projected global warming rates, strengthening of the subtropical jet and basic state lower stratospheric wind biases as predictors can explain nearly the same fraction in the CMIP6 SPV spread as the planetary wave driving (r = 0.67). The dependence of the SPV spread on the model biases in the basic state winds offers a possible emergent constraint; however, a large uncertainty prevents a substantial reduction of the projected SPV spread. The lack of this dependence in CMIP5 further calls for better understanding of underlying causes. Our results improve understanding of projected SPV uncertainty; however, further narrowing of the uncertainty remains challenging. Plain Language Summary: Previous studies showed that changes in the strength of the Northern Hemisphere wintertime stratospheric polar vortex can affect near‐surface weather on various timescales. However, climate models do not agree on whether the polar vortex will weaken or strengthen during the 21st century. Here, we use Climate Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) experiments to better understand how the polar vortex will respond to future greenhouse gas emissions. We show that changes in the propagation of large‐scale atmospheric waves can explain nearly half of the spread in the vortex strength projections by the end of the 21st century by CMIP6 models. Increased upward propagation of the waves to the stratosphere leads to vortex weakening while increased equatorward propagation within the stratosphere leads to strengthening. We identify three factors associated with projected changes in the vortex strength across CMIP6 models: projected rates of global warming, projected rates of subtropical jet stream strengthening and model errors in lower stratospheric winds in the past climate. Stronger global warming rates and stronger past lower stratospheric winds are associated with vortex strengthening, while larger strengthening of the subtropical jet stream is associated with weakening. However, these relationships are weak in CMIP5 models. Key Points: About half of the projected stratospheric polar vortex (SPV) uncertainty in Climate Model Intercomparison Project Phase 6 (CMIP6) can be attributed to stationary planetary wave drivingProjected polar vortex weakening and strengthening are linked to increased upward and equatorward wave propagation respectivelyA relationship is found between past lower stratospheric wind biases and SPV projections across CMIP6 models [ABSTRACT FROM AUTHOR]

The Canadian Earth System Model version 5.0 (CanESM5.0), the most recent major version of the global climate model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) at Environment and Climate Change Canada (ECCC), has been used extensively in climate research and for providing future climate projections in the context of climate services. Previous studies have shown that CanESM5.0 performs well compared to other models and have revealed several model biases. To address these biases, the CCCma has recently initiated the "Analysis for Development" (A4D) activity, a coordinated analysis activity in support of CanESM development. Here we describe the goals and organization of this effort and introduce two variants ("p1" and "p2") of a new CanESM version, CanESM5.1, which features important improvements as a result of the A4D activity. These improvements include the elimination of spurious stratospheric temperature spikes and an improved simulation of tropospheric dust. Other climate aspects of the p1 variant of CanESM5.1 are similar to those of CanESM5.0, while the p2 variant of CanESM5.1 features reduced equilibrium climate sensitivity and improved El Niño–Southern Oscillation (ENSO) variability as a result of intentional tuning of the atmospheric component. The A4D activity has also led to the improved understanding of other notable CanESM5.0 and CanESM5.1 biases, including the overestimation of North Atlantic sea ice, a cold bias over sea ice, biases in the stratospheric circulation and a cold bias over the Himalayas. It provides a potential framework for the broader climate community to contribute to CanESM development, which will facilitate further model improvements and ultimately lead to improved climate change information. [ABSTRACT FROM AUTHOR]

This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1. [ABSTRACT FROM AUTHOR]

Major disruptions of the winter season, high-latitude stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortex disturbances for surface predictability in subseasonal to seasonal forecast models. Based on a set of controlled, subseasonal ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models. Fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts. [ABSTRACT FROM AUTHOR]

Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt‐4xCO2 runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near‐surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift. Plain Language Summary: Previous studies showed that changes in the strength of the winds in the Northern Hemisphere wintertime stratosphere, the so‐called polar vortex, can affect near‐surface winds and precipitation on various timescales. However, climate models do not agree on whether the polar vortex will weaken or strengthen during the 21st century. Here, we use Climate Model Intercomparison Project Phase 6 experiments to better understand how the polar vortex will respond to future greenhouse gas emissions. We show that half of the uncertainty in the vortex strength projections by the end of the 21st century is due to climate model errors (model uncertainty). We show that the uncertainty in the vortex strength projections is linked to the uncertainty in projected precipitation over the Iberian Peninsula and northwestern US and projected near‐surface temperature over the western US and northern Eurasia. Most models predict an eastward shift of the vortex relative to its historical location but we do not detect any influence of the vortex longitudinal shift on surface precipitation and temperatures. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift of the vortex. Key Points: Model uncertainty contributes half of the total uncertainty in the projected strength of the Northern winter stratospheric polar vortexUncertainty in the projected polar vortex strength is linked to uncertainty in projected regional surface temperature and precipitationMost climate models project an eastward shift of the Northern winter stratospheric polar vortex [ABSTRACT FROM AUTHOR]

Sahel summertime precipitation declined from the 1950s to 1970s and recovered from the 1970s to 2000s. Anthropogenic aerosol contributions to this evolution are typically attributed to interhemispheric gradient changes of Atlantic Ocean sea surface temperature (SST). However recent work by Hirasawa et al. indicates a more complex picture, with the response being a combination of "fast" direct atmospheric (DA) processes and "slow" ocean-mediated (OM) processes. Here, we extend this understanding using the Community Atmosphere Model 5 to determine the role of regional ocean-basin perturbations and regional aerosol emission changes in the overall aerosol-driven OM and DA responses, respectively. From the 1950s to 1970s, there was an OM Sahel wetting response due to Pacific Ocean cooling that was offset by drying due to Atlantic cooling. By contrast, from the 1970s to 2000s, Atlantic trends reversed and amplified the Pacific cooling-induced wetting. This wetting was partially offset by drying driven by Indian Ocean cooling. Thus, the OM Sahel precipitation response to aerosol crucially depends on the balance of responses to Atlantic, Pacific, and Indian Ocean SST anomalies. From the 1950s to 1970s, there is DA Sahel drying that was principally due to North American aerosol emissions, with negligible effect from European emissions. DA drying from the 1970s to 2000s was mainly due to African aerosol emissions. Thus, the shifting roles of regional OM and DA effects reveal a complex interplay of direct driving and remote teleconnections in determining the time evolution of Sahel precipitation due to aerosol forcing in the late twentieth century. Significance Statement: Studies of global climate models consistently indicate that anthropogenic aerosol emissions were a significant contributor to a severe drought that occurred in the Sahel region of Africa in the late twentieth century. The drying influence of aerosol forcing is the combined result of rapid atmospheric responses directly due to the forcing and slower responses due to forced ocean temperature changes. Using a set of simulations targeted at determining the influences from different ocean basins and different emission regions for two periods in the late twentieth century, we find there is a surprising range of mechanisms through which aerosol emissions affect the Sahel. This results in a complex interplay of at times competing and at times complementary regional influences. [ABSTRACT FROM AUTHOR]

Arctic sea ice has declined rapidly over the past four decades and climate models project a seasonally icefree Arctic Ocean by the middle of this century, with attendant consequences for regional climate. However, modeling studies lack consensus on how the large-scale atmospheric circulation will respond to Arctic sea ice loss. In this study, the authors conduct a series of 200-member ensemble experiments with the Community Atmosphere Model version 6 (CAM6) to isolate the atmospheric response to past and future sea ice loss following the Polar Amplification Model Intercomparison Project (PAMIP) protocol. They find that the stratospheric polar vortex response is small compared to internal variability, which in turn influences the signal-to-noise ratio of the wintertime tropospheric circulation response to ice loss. In particular, a strong (weak) stratospheric polar vortex induces a positive (negative) tropospheric northern annular mode (and North Atlantic Oscillation), obscuring the forced component of the tropospheric response, even in 100-member averages. Stratospheric internal variability is closely tied to upward wave propagation from the troposphere and can be explained by linear wave interference between the anomalous and climatological planetary waves. Implications for the detection of recent observed trends and model realism are also presented. These results highlight the inherent uncertainty of the large-scale tropospheric circulation response to Arctic sea ice loss arising from stratospheric internal variability. [ABSTRACT FROM AUTHOR]

Over recent years there have been concomitant advances in the development of stratosphere-resolving numerical models, our understanding of stratosphere–troposphere interaction, and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing for new and improved capability in long-range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal, and annual-to-decadal climate predictions and multidecadal projections. We end with an outlook towards the future and identify areas of improvement that could further benefit these rapidly evolving predictions. [ABSTRACT FROM AUTHOR]

Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts. [ABSTRACT FROM AUTHOR]

Many recent studies have confirmed that variability in the stratosphere is a significant source of surface sub‐seasonal prediction skill during Northern Hemisphere winter. It may be beneficial, therefore, to think about times in which there might be windows‐of‐opportunity for skillfull sub‐seasonal predictions based on the initial or predicted state of the stratosphere. In this study, we propose a simple, minimal model that can be used to understand the impact of the stratosphere on tropospheric predictability. Our model purposefully excludes state dependent predictability in either the stratosphere or troposphere or in the coupling between the two. Model parameters are set up to broadly represent current sub‐seasonal prediction systems by comparison with four dynamical models from the Sub‐Seasonal to Seasonal Prediction Project database. The model can reproduce the increases in correlation skill in sub‐sets of forecasts for weak and strong lower stratospheric polar vortex states over neutral states despite the lack of dependence of coupling or predictability on the stratospheric state. We demonstrate why different forecast skill diagnostics can give a very different impression of the relative skill in the three sub‐sets. Forecasts with large stratospheric signals and low amounts of noise are demonstrated to also be windows‐of‐opportunity for skillfull tropospheric forecasts, but we show that these windows can be obscured by the presence of unrelated tropospheric signals. Plain Language Summary: For successful forecasts of surface winter conditions between two weeks and one season ahead, the stratosphere has been shown to be a key source of information. Despite many studies examining how well the stratosphere can be predicted in computer‐based forecasting systems, there remains a lack of understanding of which surface forecasts the stratosphere is most important for. This study is an attempt to step back from examining the role of the stratosphere in any particular forecasting system and instead to determine a simple framework that can be used to understand when and how the stratosphere is important. Using our framework we can construct a series of simple experiments that help to understand how important the stratosphere is in the longer range forecasting problem. Our experiments show that forecasts made during periods in which the Arctic stratospheric winds are unusually strong or weak have greater skill, but this does not depend on how unusually weak or strong the stratospheric winds are. The results are particularly important for thinking about the times in which longer range forecasts might be more skillfull than on average, so called windows‐of‐opportunity, and how these depend on the stratosphere. Key Points: We propose a model that demonstrates how forecast skill present in the lowermost stratosphere contributes to tropospheric forecast skillThe model can explain the greater correlation skill in the troposphere for forecasts during weak or strong vortex eventsThe model shows how tropospheric skill arising from the stratosphere can sometimes be confounded by uncorrelated tropospheric signals [ABSTRACT FROM AUTHOR]

Over recent years there have been parallel advances in the development of stratosphere resolving numerical models, our understanding of stratosphere-troposphere interaction and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing new and improved capability in long range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal and decadal climate predictions. We end with an outlook towards the future of climate forecasts and identify areas for improvement that could further benefit these rapidly evolving predictions. [ABSTRACT FROM AUTHOR]

Given uncertainty in the processes involved in polar amplification, elucidating the role of poleward heat and moisture transport is crucial. The Polar Amplification Model Intercomparison Project (PAMIP) permits robust separation of the effects of sea ice loss from sea surface warming under climate change. We utilize a moist isentropic circulation framework that accounts for moisture transport, condensation, and eddy transport, in order to analyze the circulation connecting the mid‐latitudes and the Arctic. In PAMIP's atmospheric general circulation model experiments, prescribed sea ice loss reduces poleward heat transport (PHT) by warming the returning moist isentropic circulation at high latitudes, while prescribed warming of the ocean surface increases PHT by strengthening the moist isentropic circulation. Inter‐model spread of PHT into the Arctic reflects the tug‐of‐war between sea‐ice and surface‐warming effects. Plain Language Summary: A major conundrum in current climate science is to understand what Arctic changes imply for the climate and environment in mid‐latitude regions. The Polar Amplification Model Intercomparison Project (PAMIP) designed a set of climate model experiments to specifically answer this question in a carefully designed, idealized framework. PAMIP's approach is to separate historic and projected climate change into parts associated with Arctic sea ice loss and ocean surface warming and investigate how these two contributions can influence the atmosphere. To isolate these effects, only models can be used, because, in reality, sea ice loss and ocean surface warming are strongly linked together. This letter focuses on what the PAMIP experiments imply for the transport and redistribution of heat and moisture in the atmosphere. In PAMIP, we learn that Arctic sea ice loss causes the atmosphere to reduce the transport of dry and cold air away from the Arctic while ocean warming causes more transport of moist warm air toward the Arctic. These two effects are in a tug‐of‐war, suggesting that climate change can cause a mix of competing impacts on the global energy transport from the tropics to the Arctic. Key Points: Sea ice loss exports static energy from the Arctic by warming the equatorward branch of the global mass circulationSea surface warming imports static energy into the Arctic by increasing the mid‐latitude mass transport, overwhelming the sea ice effectThere is more spread amongst models for the effect of sea‐surface warming than for sea ice loss [ABSTRACT FROM AUTHOR]

Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (-20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes. SIGNIFICANCE STATEMENT: As Earth warms, the Arctic is shifting toward a longer open water season. With maritime access consequently increasing, stakeholders are valuing trustworthy information on the timing of transitional sea ice cover provided by seasonal forecasting models. In this study we advance seasonal predictions of the timing of local ice retreat and advance by extending these predictions to include critical information on forecast uncertainty. To do this, we tailor the established ''ensemble model output statistics" calibration framework to sea ice retreat and advance dates, and construct probabilistic forecasts of early, normal, and late sea ice timing. Evaluating these predictions over a historical period indicates that stakeholders can place trust in forecast probabilities of sea ice timing for planning purposes. [ABSTRACT FROM AUTHOR]

Recent studies suggest that the wintertime North Pacific eddy-driven jet stream will strengthen and extend eastward in response to Arctic sea ice loss. Using output from the Polar Amplification Model Intercomparison Project we examine the mean change of the North Pacific wintertime zonal winds, and use cluster analysis to explore the change in sub-seasonal, wintertime variability in zonal winds between experiments with future Arctic sea ice concentrations relative to a pre-industrial run. Further, given the relationship between the North Pacific jet stream and North American weather regimes, we also examine the changes in surface temperature variability over North America. The four climate models investigated here exhibit robust agreement in both sign and structure of the atmospheric responses, with a strengthened wintertime North Pacific jet, an increase in anomalously strong and extended jet events, and a decreased frequency of weakened and equatorward-shifted jet events in response to reduced Arctic sea ice. The models also show changes in wintertime, North American surface temperature patterns that are consistent with the zonal wind changes seen in the North Pacific. There is an increase in the frequency of occurrence of the North American temperature dipole pattern, defined as anomalously warm temperatures in the west or northwest and anomalously cold temperatures in the east or southeast, and a decrease in the frequency of anomalously cold temperatures over North America. [ABSTRACT FROM AUTHOR]

The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8 ∘) and ocean (nominally 1 ∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada. [ABSTRACT FROM AUTHOR]

Polar amplification – the phenomenon where external radiative forcing produces a larger change in surface temperature at high latitudes than the global average – is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the sixth Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: (1) what are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? (2) How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present-day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower-priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: what role does ocean–atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What have been the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real-world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking "emergent constraints" in which model uncertainty may be reduced by using an observable quantity that physically explains the intermodel spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability. [ABSTRACT FROM AUTHOR]

Abstract: Anthropogenic‐aerosol (AA) radiative forcing modulates multidecadal greenhouse radiative forcing. However, decadal climate responses to AA are poorly characterized given AA forcing uncertainty and internal climate variability. This motivates revisiting a recent claim that AA drove a negative trend in the Pacific Decadal Oscillation and an associated cooling influence in the 10–15 years following the late‐1990's El Niño. The average of a 50‐member initial condition ensemble of the second generation Canadian Earth System Model version 2 that was forced only with AA does not exhibit the negative‐Pacific Decadal Oscillation/slowdown response. However, spurious responses of this kind, that are artifacts of subsetting the large ensemble (LE) in a manner consistent with published literature, can readily be found. This illustrates the caution needed in interpreting regional‐ and decadal‐scale responses to AA and suggests that improved characterization of model uncertainty in AA over the recent period is required. [ABSTRACT FROM AUTHOR]

Polar amplification - the phenomenon that external radiative forcing produces a larger change in surface temperature at high latitudes than the global average - is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the Sixth Coupled Model Intercomparison Project (CMIP6, Eyring et al. 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: 1. What are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? 2. How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: What role does ocean-atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What are the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking "emergent constraints" in which model uncertainty may be reduced by using an observable quantity that physically explains the inter-model spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability. [ABSTRACT FROM AUTHOR]

The Canadian Sea Ice and Snow Evolution (Can- SISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time. [ABSTRACT FROM AUTHOR]

Recent decades have seen significant developments in climate prediction capabilities at seasonal-tointerannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initialvalue predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions.We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño-Southern Oscillation. [ABSTRACT FROM AUTHOR]

The first multimodel study to estimate the predictability of a boreal sudden stratospheric warming (SSW) is performed using five NWP systems. During the 2012/13 boreal winter, anomalous upward propagating planetary wave activity was observed toward the end of December, which was followed by a rapid deceleration of the westerly circulation around 2 January 2013, and on 7 January 2013 the zonal-mean zonal wind at 60°N and 10 hPa reversed to easterly. This stratospheric dynamical activity was followed by an equatorward shift of the tropospheric jet stream and by a high pressure anomaly over the North Atlantic, which resulted in severe cold conditions in the United Kingdom and northern Europe. In most of the five models, the SSW event was predicted 10 days in advance. However, only some ensemble members in most of the models predicted weakening of westerly wind when the models were initialized 15 days in advance of the SSW. Further dynamical analysis of the SSW shows that this event was characterized by the anomalous planetary wavenumber-1 amplification followed by the anomalous wavenumber-2 amplification in the stratosphere, which resulted in a split vortex occurring between 6 and 8 January 2013. The models have some success in reproducing wavenumber-1 activity when initialized 15 days in advance, but they generally failed to produce the wavenumber-2 activity during the final days of the event. Detailed analysis shows that models have reasonably good skill in forecasting tropospheric blocking features that stimulate wavenumber-2 amplification in the troposphere, but they have limited skill in reproducing wavenumber-2 amplification in the stratosphere. [ABSTRACT FROM AUTHOR]

Extreme variability of the winter- and spring-time stratospheric polar vortex has been shown to affect extratropical tropospheric weather. Therefore, reducing stratospheric forecast error may be one way to improve the skill of tropospheric weather forecasts. In this review, the basis for this idea is examined. A range of studies of different stratospheric extreme vortex events shows that they can be skilfully forecasted beyond 5 days and into the sub-seasonal range (0-30 days) in some cases. Separate studies show that typical errors in forecasting a stratospheric extreme vortex event can alter tropospheric forecast skill by 5-7% in the extratropics on sub-seasonal time-scales. Thus understanding what limits stratospheric predictability is of significant interest to operational forecasting centres. Both limitations in forecasting tropospheric planetary waves and stratospheric model biases have been shown to be important in this context. [ABSTRACT FROM AUTHOR]

A robust feature of the observed response to El Niño-Southern Oscillation (ENSO) is an altered circulation in the lower stratosphere. When sea surface temperatures (SSTs) in the tropical Pacific are warmer there is enhanced upwelling and cooling in the tropical lower stratosphere and downwelling and warming in the midlatitudes, while the opposite is true of cooler SSTs. The midlatitude lower stratospheric response to ENSO is larger in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH). In this study the dynamical version of the Canadian Middle Atmosphere Model (CMAM) is used to simulate 25 realizations of the atmospheric response to the 1982/83 El Niño and the 1973/74 La Niña. This version of CMAM is a comprehensive high-top general circulation model that does not include interactive chemistry. The observed lower stratospheric response to ENSO is well reproduced by the simulations, allowing them to be used to investigate the mechanisms involved. Both the observed and simulated responses maximize in December-March and so this study focuses on understanding the mechanisms involved in that season. The response in tropical upwelling is predominantly driven by anomalous transient synoptic-scale wave drag in the SH subtropical lower stratosphere, which is also responsible for the compensating SH midlatitude response. This altered wave drag stems from an altered upward flux of wave activity from the troposphere into the lower stratosphere between 20° and 40°S. The altered flux of wave activity can be divided into two distinct components. In the Pacific, the acceleration of the zonal wind in the subtropics from the warmer tropical SSTs results in a region between the midlatitude and subtropical jets where there is an enhanced source of low phase speed eddies. At other longitudes, an equatorward shift of the midlatitude jet from the extratropical tropospheric response to El Niño results in an enhanced source of waves of higher phase speeds in the subtropics. The altered resolved wave drag is only apparent in the SH and the difference between the two hemispheres can be related to the difference in the climatological jet structures in this season and the projection of the wind anomalies associated with ENSO onto those structures. [ABSTRACT FROM AUTHOR]