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Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change 1 – 3 . Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain 4 . This leads to low confidence in regional projections, especially for precipitation, over the coming decades 5 , 6 . The chaotic nature of the climate system 7 – 9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models 10 , and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. Current models are too noisy to predict climate usefully on decadal timescales, but two-stage post-processing of model outputs greatly improves predictions of decadal variations in North Atlantic winter climate.

The impact of climate change on Sahel precipitation suffers from large uncertainties and is strongly model-dependent. In this study, we analyse sources of inter-model spread in Sahel precipitation change by decomposing precipitation into its dynamic and thermodynamic terms, using a large set of climate model simulations. Results highlight that model uncertainty is mostly related to the response of the atmospheric circulation to climate change (dynamic changes), while thermodynamic changes are less uncertain among climate models. Uncertainties arise mainly because the models simulate different shifts in atmospheric circulation over West Africa in a warmer climate. We linked the changes in atmospheric circulation to the changes in Sea Surface Temperature, emphasising that the Northern hemispheric temperature gradient is primary to explain uncertainties in Sahel precipitation change. Sources of Sahel precipitation uncertainties are shown to be the same in the new generation of climate models (CMIP6) as in the previous generation of models (CMIP5).

The Emirates Mars Mission (EMM) – Hope Probe – was developed to understand Mars atmospheric circulation, dynamics, and processes through characterization of the Mars atmosphere layers and its interconnections enabled by a unique high-altitude (19,970 km periapse and 42,650 km apoapse) low inclination orbit that will offer an unprecedented local and seasonal time coverage over most of the planet. EMM has three scientific objectives to (A) characterize the state of the Martian lower atmosphere on global scales and its geographic, diurnal and seasonal variability, (B) correlate rates of thermal and photochemical atmospheric escape with conditions in the collisional Martian atmosphere, and (C) characterize the spatial structure and variability of key constituents in the Martian exosphere. The EMM data products include a variety of spectral and imaging data from three scientific instruments measuring Mars at visible, ultraviolet, and infrared wavelengths and contemporaneously and globally sampled on both diurnal and seasonal timescale. Here, we describe our strategies for addressing each objective with these data in addition to the complementary science data, tools, and physical models that will facilitate our understanding. The results will also fill a unique role by providing diagnostics of the physical processes driving atmospheric structure and dynamics, the connections between the lower and upper atmospheres, and the influences of these on atmospheric escape.

In order to study the interactions between the atmospheric circulations at the middle-high and low latitudes from the global perspective, the authors proposed the mathematical definition of three-pattern circulations, i.e., horizontal, meridional and zonal circulations with which the actual atmospheric circulation is expanded. This novel decomposition method is proved to accurately describe the actual atmospheric circulation dynamics. The authors used the NCEP/NCAR reanalysis data to calculate the climate characteristics of those three-pattern circulations, and found that the decomposition model agreed with the observed results. Further dynamical analysis indicates that the decomposition model is more accurate to capture the major features of global three dimensional atmospheric motions, compared to the traditional definitions of Rossby wave, Hadley circulation and Walker circulation. The decomposition model for the first time realized the decomposition of global atmospheric circulation using three orthogonal circulations within the horizontal, meridional and zonal planes, offering new opportunities to study the large-scale interactions between the middle-high latitudes and low latitudes circulations.

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. Plain Language Summary California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems. Key Points A hybrid dynamical‐statistical model is described for 1–4‐week forecasts of impactful California winter weather using circulation regimes This hybrid framework reduces the number of forecasts produced, but the ones issued can be interpreted with higher confidence This new methodology provides skillful subseasonal forecasts with potential to improve early warnings for impactful weather events

Multidecadal variations in the global land monsoon were observed during the twentieth century, with an overall increasing trend from 1901 to 1955 that was followed by a decreasing trend up to 1990, but the mechanisms governing the above changes remain inconclusive. Based on the outputs of two atmospheric general circulation models (AGCMs) forced by historical sea surface temperature (SST) covering the twentieth century, supplemented with AGCM simulations forced by idealized SST anomalies representing different conditions of the North Atlantic and tropical Pacific, evidence shows that the observed changes can be partly reproduced, particularly over the Northern Hemisphere summer monsoon (NHSM) domain, demonstrating the modulation of decadal SST changes on the long-term variations in monsoon precipitation. Moisture budget analysis is performed to understand the interdecadal changes in monsoon precipitation, and the dynamic term associated with atmospheric circulation changes is found to be prominent, while the contribution of the thermodynamic term associated with humidity changes can lead to coincident wetting over the NHSM domain. The increase (decrease) in NHSM land precipitation during 1901–55 (1956–90) is associated with the strengthening (weakening) of NHSM circulation and Walker circulation. The multidecadal scale changes in atmospheric circulation are driven by SST anomalies over the North Atlantic and the Pacific. A warmer North Atlantic together with a colder eastern tropical Pacific and a warmer western subtropical Pacific can lead to a strengthened meridional gradient in mid-to-upper-tropospheric thickness and strengthened trade winds, which transport more water vapor into monsoon regions, leading to an increase in monsoon precipitation.

Accurate long‐term flood predictions are increasingly needed for flood risk management in a changing climate, but are hindered by the underestimation of climate variability by climate models. Here, we drive a statistical flood model with a large ensemble of dynamical CMIP5‐6 predictions of precipitation and temperature. Predictions of UK winter flooding (95th streamflow percentile) have low skill when using the raw 676‐member ensemble averaged over lead times of 2–5 years from the initialization date. Sub‐selecting 20 ensemble members that adequately represent the multiyear temporal variability in the North Atlantic Oscillation (NAO) significantly improves the flood predictions. Applying this method we show positive skill in 46% of stations compared to 26% using the raw ensemble, primarily in regions most strongly influenced by the NAO. Our findings reveal the potential of decadal predictions to inform flood risk management at long lead times. Plain Language Summary Reliable predictions of flooding can help society to manage the associated risk to lives and property. Seasonal predictions of flooding over the coming months already form the basis of many operational services around the world. In contrast, decadal predictions with lead times of up to 10 years are more challenging, due to the difficulty of simulating dynamic changes in atmospheric circulation at these timescales. Here, we show that a large ensemble of climate models can predict average winter flood conditions over the UK in the next decade. The climate models underestimate the magnitude of atmospheric variability in the north Atlantic and identifying a subset of skillful climate model simulations improves the ability to predict floods. Our results suggest that decadal climate predictions may be useful in the context of flood risk management. However, the use of multiyear averages for flood prediction is still poorly studied, and therefore further work should help determine how such predictions can be used in an operational setting. Key Points Multiyear predictions of mean winter floods 2–5 years ahead are skillful across much of the UK Skill is improved by “NAO‐matching” to overcome spuriously weak modeled signals The higher the sensitivity of streamflow to the North Atlantic Oscillation at a given gauge, the greater the benefit of NAO‐matching for decadal flood prediction

Changes in summer precipitation over the Tibetan Plateau (TP) significantly influence the surface runoff, river discharge and water availability for the downstream Asian countries, which is sensitive to external forcing. But its response to volcanic eruptions remains unknown. Here we investigate the summer precipitation changes after tropical volcanic eruptions over the TP region by using multiple lines of evidence including reconstructions over the last hundreds of years, observations during recent decades and model simulations covering the last millennium. Both the instrumental data and reconstructions reveal a significant reduction in summer precipitation over the southern TP region during the first summer following tropical volcanic eruptions, which are further confirmed by the coupled model simulations driven by volcanic forcing. The model results indicate that both the dynamic processes related to atmospheric circulation changes and the thermodynamic processes related to specific humidity changes contribute to the decreased precipitation in the southwestern TP, while the thermodynamic process dominates the reduction of precipitation in the southeastern TP. The thermodynamic process results from decreased atmospheric precipitable water caused by decreased surface temperature after tropical volcanic eruptions. The dynamic processes are caused by increased gross moist stability, spatial distribution of surface cooling and a southward shift of westerlies related to weakening and shrinking of Hadley circulation following tropical eruptions. Our results imply that major tropical eruptions have significant impact on the summer precipitation over the southern TP regions, which will further decrease the source of supply for the TP glaciers and runoff output.

Most of the globe has experienced significant warming trends that have been attributed to anthropogenic climate change. However, these rates of warming are also influenced by short-term climate fluctuations driven by atmospheric circulation dynamics, resulting in inconsistent trend magnitudes in both time and space. This research evaluated winter (December–February) temperature trends over 1950–2020 at 91 climate stations across British Columbia (BC), Alberta (AB), and Saskatchewan (SK), Canada, and determined the components attributed to thermodynamic and dynamic (atmospheric circulation) factors. A synoptic climatological approach was used to classify atmospheric circulation patterns in the midtroposphere, relate those patterns to surface temperature, and evaluate changes in frequency. Moderate to high temperature increases over 71 years were found for most of the region, averaging 3.1°C in southern SK to 4.1°C in central-northern AB, and a maximum of 5.8°C in northern BC. Low to moderate increases were found for southern BC, averaging 1.2°C. Changes in atmospheric circulation accounted for 29% and 31% of observed temperature changes in central-northern BC and AB, respectively. Dynamic factors were a moderate driver in southern AB (18%) and central-northern SK (13%), and low in southern SK (5%). Negative dynamic contributions in southern BC (−6%), suggest that atmospheric circulation changes counteracted thermodynamically driven temperature changes. Results were consistent with trend analyses, indicating this method is well suited for trend detection and identification of thermodynamic and dynamic drivers. Results of this research improve our understanding of the magnitude of winter temperature changes critical for informing adaptation and climate-related policy decisions.

Performance in simulating atmospheric rivers (ARs) over western North America based on AR frequency and landfall latitude is evaluated for 10 models from phase 5 of the Coupled Model Intercomparison Project among which the CanESM2 model performs well. ARs are classified into southern, northern, and middle types using self-organizing maps in the ERA-Interim reanalysis and CanESM2. The southern type is associated with the development and eastward movement of anomalous lower pressure over the subtropical eastern Pacific, while the northern type is linked with the eastward movement of anomalous cyclonic circulation stimulated by warm sea surface temperatures over the subtropical western Pacific. The middle type is connected with the negative phase of North Pacific Oscillation–west Pacific teleconnection pattern. CanESM2 is further used to investigate projected AR changes at the end of the twenty-first century under the representative concentration pathway 8.5 scenario. AR definitions usually reference fixed integrated water vapor or integrated water vapor transport thresholds. AR changes under such definitions reflect both thermodynamic and dynamic influences. We therefore also use a modified AR definition that isolates change from dynamic influences only. The total AR frequency doubles compared to the historical period, with the middle AR type contributing the largest increases along the coasts of Vancouver Island and California. Atmospheric circulation (dynamic) changes decrease northern AR type frequency while increasing middle AR type frequency, indicating that future changes of circulation patterns modify the direct effect of warming on AR frequency,which would increase ARs (relative to fixed thresholds) almost everywhere along the North American coastline.