Search

In this paper, X-band COSMO-SkyMed© Synthetic Aperture Radar (SAR) data are first experimented as wind field forcing of coastal wind-wave oceanographic modeling for both sea-wave numerical simulation and coastal vulnerability assessment purposes. The SAR data set consists of 60 X-band VV-polarized Level 1B Detected Ground Multilook (DGM) ScanSAR Huge Region COSMO-SkyMed© SAR measurements, collected in the test area of the Southern Tyrrhenian Coastal basin during the winter season of 2010. On one hand, the wind-wave oceanographic modeling is based on the thirdgeneration Simulating WAves Nearshore (SWAN) model, which is used for sea wave state estimation in coastal and island regions. On the other hand, the coastal vulnerability assessment model is based on the use of a key parameter known as impact index, which consistently provides the coastal risk evaluation due to the inundation of the inshore land. Experiments consist of numerical wave simulations of the SWAN model accomplished with respect to some relevant wave storms recorded in the test area during the winter season of 2010. The wind forcing is provided by X-band COSMO-SkyMed© SAR-based wind field estimations which are properly blended with both buoys wave data and ECMWF model winds to retrieve meaningful wave parameters (e.g. significant wave height, wave directions and periods) as physical descriptors of tidal events. The output of numerical wave simulations are used to perform the coastal vulnerability assessment in the considered test area along the coastal plain of river Sele. The assessment is accomplished in terms of wave run-up height, storm beach retreat and both short- and long-term erosion shoreline evolution. Experimental results accomplished with X-band COSMO-SkyMed© SAR-based wind field forcing are successfully compared with the ones gathered by using both buoys wave field data and ECMWF model winds, only. They demonstrate that both wind-wave interaction modeling and coastal vulnerability assessment can take full benefits of blended wind field products composed by X-band COSMO-SkyMed© SAR wind field estimations and model data. [ABSTRACT FROM AUTHOR]

We describe the development, tuning and climate of PLASIM-GENIE, a new intermediate complexity Atmosphere-Ocean Global Climate Model (AOGCM), built by coupling the Planet Simulator to the GENIE earth system model. PLASIM-GENIE supersedes "GENIE-2", a coupling of GENIE to the Reading IGCM. It has been developed to join the limited number of models that bridge the gap between EMICS with simplified atmospheric dynamics and state of the art AOGCMs. A 1000 year simulation with PLASIM-GENIE requires approximately two weeks on a single node of a 2.1 GHz AMD 6172 CPU. An important motivation for intermediate complexity models is the evaluation of uncertainty. We here demonstrate the tractability of PLASIM-GENIE ensembles by deriving a "subjective" tuning of the model with a 50 member ensemble of 1000 year simulations. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed. [ABSTRACT FROM AUTHOR]

In this paper, X-band COSMO-SkyMed© SAR wind field retrieval is investigated to force coastal circulation modeling. The SAR data set consists of 60 X-band Level 1B Multi-Look Ground Detected ScanSAR Huge Region COSMO-SkyMed© SAR data, gathered in the Southern Tyrrhenian Sea during the Summer and Winter seasons of 2010. The SAR-based wind vector field estimation is accomplished by resolving both the SAR-based wind speed and wind direction retrieval problems independently. The sea surface wind speed is retrieved by means of a SAR wind speed algorithm based on the Azimuth cut-off procedure, while the sea surface wind direction is provided by means of a SAR wind direction algorithm based on the Discrete Wavelet Transform Multi-Resolution Analysis. The obtained wind fields are compared with ground truth data provided by both ASCAT scatterometer and ECMWF model wind fields. SAR-derived wind vector fields and ECMWF model wind data are used to construct a blended wind product regularly sampled in both space and time, which is then used to force a coastal circulation model of a Southern Tyrrhenian coastal area to simulate wind-driven circulation processes. The modeling results clearly show that X-band COSMO-SkyMed© SAR data can be valuable in providing effective wind fields for coastal circulation modeling. [ABSTRACT FROM AUTHOR]

The sea surface temperature (SST) anomalies in the South China Sea (SCS) and their influences on global atmospheric circulation were studied. The results of the simple atmospheric model suggested that the SCS SST anomalies can induce several barotropic wave trains from the SCS to other regions such as North America, high latitudes of the Southern Hemisphere and the Mediterranean. The baroclinic stream function anomalies from the simple model showed an anticyclonic vortex pair in East Asia and southern tropical Indian Ocean and a cyclonic vortex pair in the North Pacific and the Southwest Pacific. It is suggested that the spatial pattern of SST anomalies in the SCS can affect the magnitude of stream function anomalies, although it cannot affect the spatial pattern of atmospheric circulation. [ABSTRACT FROM AUTHOR]

The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation. Plain Language Summary: Many properties of the atmosphere are affected by the temperature of the ocean surface. Knowing how strong these effects are would help us to better predict global warming. The response to a given surface warming depends on where the warming occurs. To account for this, researchers sometimes simulate the response to individual patches of warming and then assume the response to an arbitrary warming pattern can be summed together from these patch responses. This is sometimes called the atmospheric Green's function method, and it works well at recreating the atmospheric response to historical temperature changes. We are organizing a Green's Function Model Intercomparison Project (GFMIP), in which participants will apply the method consistently for many climate models. This paper presents the GFMIP protocol. In the course of developing this protocol, we found that the atmospheric response to warming is not proportional in all cases: the response to surface warming is not the opposite of the response to surface cooling; warming twice as much doesn't cause twice as much of a response; and making a patch of warming twice as large doesn't cause twice as large a response. GFMIP will help us figure out how to account for this nonlinearity. Key Points: The Green's Function Model Intercomparison Project (GFMIP) explores the atmospheric response to surface temperature patch perturbationsThis paper presents the GFMIP protocol, which was generated using insights from past studies and new sensitivity testsGreen's functions reconstruct the response to historical temperatures, but nonlinearities can affect responses to other warming patterns [ABSTRACT FROM AUTHOR]

In order to study the imprints of solar-ENSO-geomagnetic activity on the Indian Subcontinent, we have applied the Singular Spectral Analysis (SSA) and wavelet analysis to the tree ring temperature variability record from the western Himalayas. The data used in the present study are the Solar Sunspot Number (SSN), Geomagnetic Indices (aa Index), Southern Oscillation Index (SOI) and tree ring temperature record from western Himalayas (WH), for the period of 1876-2000. The SSA and wavelet spectra reveal the presence of 5 years short term ENSO variations to 11 year solar cycle indicating the influence of both the solar-geomagnetic and ENSO imprints in the tree ring data. The presence of 33-year cycle periodicity suggests the Sun-temperature variability probably involving the induced changes in the basic state of the atmosphere. Our wavelet analysis for the SSA reconstructed time series agrees with our previous results and also enhance the amplitude of the signals by removing the noise and showing a strong influence of solar-geomagnetic and ENSO patterns throughout the record. The solar flares are considered to be responsible for cause in the circulation patterns in the atmosphere. The net effect of solar-geomagnetic processes on temperature record thus appears to be the result of counteracting influences on shorter (about 5-6 years) and longer (about 11-12 years) time scales. The present analysis thus suggests that the influence of solar processes on Indian temperature variability operates in part indirectly through ENSO, but on more than one time scale. The analyses hence provides credible evidence for teleconnections of tropical pacific climatic variability with Indian climate ranging from interannual-decadal time scales and also demonstrate the possible role of exogenic triggering in reorganizing the global earth-ocean-atmospheric systems. [ABSTRACT FROM AUTHOR]

We describe the theoretical basis, implementation and validation of a new parametrisation that accounts for the effect of large offshore wind farms on the atmosphere and can be used in mesoscale and large-scale atmospheric models. This new parametrisation, referred to as the Explicit Wake Parametrisation (EWP), uses classical wake theory to describe the unresolved wake expansion. The EWP scheme is validated against filtered in situ measurements from two meteorological masts situated a few kilometres away from the Danish offshore wind farm Horns Rev I. The simulated velocity deficit in the wake of the wind farm compares well to that observed in the measurements and the velocity profile is qualitatively similar to that simulated with large eddy simulation models and from wind tunnel studies. At the same time, the validation process highlights the challenges in verifying such models with real observations. [ABSTRACT FROM AUTHOR]

Recently, a standard test case suite for 2-D linear transport on the sphere was proposed to assess important aspects of accuracy in geophysical fluid dynamics with a "minimal" set of idealized model configurations/runs/diagnostics. Here we present results from 19 state-of-the-art transport scheme formulations based on finite-difference/finite-volume methods as well as emerging (in the context of atmospheric/ oceanographic sciences) Galerkin methods. Discretization grids range from traditional regular latitude-longitude grids to more isotropic domain discretizations such as icosahedral and cubed-sphere tessellations of the sphere. The schemes are eval10 uated using a wide range of diagnostics in idealized flow environments. Accuracy is assessed in single- and two-tracer configurations using conventional error norms as well as novel diagnostics designed for climate and climate-chemistry applications. In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent, The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data-sets are made available to facilitate the process of model evaluation and scheme intercomparison. [ABSTRACT FROM AUTHOR]

It is shown that a recently developed hybrid modeling approach that combines machine learning (ML) with an atmospheric global circulation model (AGCM) can serve as a basis for capturing atmospheric processes not captured by the AGCM. This power of the approach is illustrated by three examples from a decades‐long climate simulation experiment. The first example demonstrates that the hybrid model can produce sudden stratospheric warming, a dynamical process of nature not resolved by the low resolution AGCM component of the hybrid model. The second and third example show that introducing 6‐hr cumulative precipitation and sea surface temperature (SST) as ML‐based prognostic variables improves the precipitation climatology and leads to a realistic ENSO signal in the SST and atmospheric surface pressure. Plain Language Summary: This paper introduces and tests schemes for efficiently enabling significant expansion of the utility and scope of a recently introduced hybrid modeling technique that combines machine learning with an atmospheric global circulation model (AGCM). Simulation experiments are carried out with an implementation of the approach on a low resolution simplified AGCM. An examination of the simulated atmospheric circulation suggests that the hybrid model can capture dynamical process not captured by the AGCM. Moreover, the addition of precipitation and sea surface temperature (SST) as machine learning predicted physical quantities to the model improves the precipitation climatology and leads to a realistic El Niño‐La Niña signal in the SST and atmospheric surface pressure. Key Points: A hybrid system combining an atmospheric global circulation model (AGCM) with a machine‐learning component can capture processes not captured by the AGCMMachine learning provides a flexible framework to introduce additional prognostic variables into the hybrid modelThe prototype hybrid model tested in the paper is stable and has a realistic climate in decades‐long simulation experiments [ABSTRACT FROM AUTHOR]

This paper expands on work showing that the winter North Atlantic Oscillation (NAO) is predictable on decadal timescales to quantify the skill in capturing the North Atlantic eddy‐driven jet's location and speed. By focusing on decadal predictions made for years 2–9 from the sixth Coupled Model Intercomparison Project over 1960–2005 we find that there is significant skill in jet latitude and, especially, jet speed associated with the skill in the NAO. However, the skill in the NAO, jet latitude and speed indices appears to be sensitive to the period over which it is assessed. In particular, skill drops considerably when evaluating hindcasts up to the present day as models fail to capture the recent observed northern shift and strengthening of the winter eddy‐driven jet, and more thus positive NAO. We suggest that the drop in atmospheric circulation skill is related to reduced skill in North Atlantic sea surface temperatures. Plain Language Summary: Climate models have the capability to predict the evolution of mean atmospheric circulation over long timescales, from annual to decadal and longer. However, models are overestimating the chaotic, unpredictable component of the climate's variability and, although model predictions follow the observed oscillations of the climate, the strength of these oscillations is critically underestimated. While climate models have demonstrated skill in predicting the North Atlantic Oscillation (NAO), this paper assesses model skill in predicting the evolution of the wintertime North Atlantic eddy‐driven jet, with the aim to highlight how much of the predictive skill of the NAO is derived from accurate predictions of the jet's state. We find levels of skill similar to that for the NAO, with slightly higher skill for the jet's strength (speed) over its location (latitude of maximum speed). We also notice a drop in skill over the last decade, as models fail to capture the latest trends in the NAO and jet's evolution, and suggest that it might be related to degradation in skill at predicting surface temperature variability. Key Points: The winter North Atlantic eddy‐driven jet is predictable on decadal time‐scales with skill in jet speed comparable to that for the North Atlantic Oscillation (NAO)Anomalies in the jet are substantially smaller than expected from skill alone and so suffer from noise overestimationSkill drops significantly in the last decade, as hindcasts do not capture the return to positive NAO conditions post‐2010 [ABSTRACT FROM AUTHOR]

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Köppen climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes, the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is changing the earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose to investigate future hazards. BARPA-R, a limited area model, has a 17 km horizontal grid-spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA-5 reanalysis. Here, an assessment of this evaluation experiment is provided. First, an examination of BARPA-R's representation of Australia's surface air temperature, rainfall and 10-m winds finds good performance overall, with biases including a 1K cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm/month in inland Australia. Recent trends in diurnal maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in rainfall show underestimated wetting in northern Australia. Rainfall and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10-metre winds are improved over ERA5 in six out of eight of the Australian regions considered. The second section of the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the SPCZ, Northwest Cloud-Bands and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extra-tropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 GCMs. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. [ABSTRACT FROM AUTHOR]

The frequency of flash floods resulting from heavy rainfall over West Africa has increased in recent years with serious socio‐economic consequences. Therefore, the need to utilize numerical weather prediction models to forecast heavy rainfall events reliably is also rising at many operational meteorological centres in West Africa. This paper evaluates the performance of the Consortium for Small‐scale Modelling (COSMO) model of the German Meteorological Services (DWD) in predicting rainfall over West Africa for high‐impact rainfall events that occurred between 19 and 26 August 2017. The paper aims to investigate the synoptic forcings modulating daily rainfall variability during that period. Results show that COSMO simulates adequately the spatio‐temporal variability of rainfall distribution over West Africa, though with inherent biases. COSMO displays a decreasing skill in producing spatial rainfall distribution as rainfall amounts tend to 30 mm and above. Additionally, areas of heavy rainfall, mostly about 100–300 km southwest of the core of the Africa Easterly Jet (AEJ), often coincide with areas of decreasing mean sea level pressure of at least 0.6 hPa and areas of increasing convective available potential energy of at least 500 J/kg. Although not in all cases, the trough of the Africa Easterly Wave (AEW) is always located to the east of these areas. We show that not every storm, especially east of the prime meridian, is associated with an AEW trough. COSMO is able to reproduce the atmospheric dynamics modulating the daily rainfall variability, in addition to capturing the daily propagation of the AEW trough, and the core of the AEJ. However, the reproducibility skill of the model in predicting atmospheric dynamics may not transform into the predictive skill of the model in producing rainfall. Nevertheless, operational forecasters may be able to determine likely areas of heavy rainfall by estimating the position of the AEJ core based on the position of areas of the least falling pressure from COSMO. Finally, the incorporation of the fractions skill score metric based on the neighbourhood approach could also assist operational forecasters to decide at which scale a severe weather alert can be issued. [ABSTRACT FROM AUTHOR]

The article discusses a model used to examine the ascent of sounding balloons, which are used in meteorological research and forecasting, in the lower stratosphere and the troposhere. It states that the model takes into consideration the variation of the drag coefficient with altitude as well as the heat imbalance between the atmosphere and the balloon. It further explores the extraction of the air vertical velocity.

The paper discusses issues related to the main features of subseasonal variability in the tropics, as well as the influence of the Madden–Julian oscillation (MJO) on atmospheric processes in the Northern Hemisphere mid-latitudes. To identify weather patterns in the tropics, the RMM (Real-time Multivariate) index proposed by M. Wheeler and H. Hendon is used. Teleconnection indices are used to quantify atmospheric processes in the Northern Hemisphere mid-latitudes. The analysis of variance leads to the conclusion that there are teleconnections between the weather patterns in the tropics and the mid-latitudes at time intervals of about 5–7 days, which are most clearly pronounced in the Pacific–North American region, as well as in Asia. The MJO effect on the circulation in the Euro-Atlantic sector is quite complex and requires consideration of additional factors, in particular, the circulation in the stratosphere and El Niño events. Using the INM-CM5 climate model (Russia), the potential of predicting the MJO over a three-week time interval is demonstrated. The results are expected to be used in the operational practice of the North Eurasia Climate Center. [ABSTRACT FROM AUTHOR]

The study of climate and its change had traditionally been descriptive and regionally oriented and regarded as a minor branch of meteorology. With advances in computing technology, however, climate studies gradually moved towards highly mathematical endeavors. By the mid-1970s, climate research became dominated by dynamical meteorologists, atmospheric physicists, and physical oceanographers armed with complex numerical modeling as a principal methodology. The observational studies of climate, as well as simple climate modeling studies focusing on climate sensitivity and feedback processes, continued to play an essential role in raising the issue of climate change -- both natural and anthropogenic -- in the 1960s and early 1970s. On the other hand, many meteorologists firmly believed that climate and its change could only be properly studied using numerical models of the large-scale atmospheric circulation -- i.e., general circulation models (GCMs). This tendency was particularly strong in Britain. From early on, the British Meteorological Office devoted much of its climate research efforts to developing GCMs. This paper traces the history of GCM developments at the British Meteorological Office from the 1950s to the mid-1970s and also explores how particular styles of scientific practice, disciplinary traditions, institutional cultures, and broader political environments shaped these developments. [ABSTRACT FROM AUTHOR]

We introduce ModE-Sim, a medium size ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR version (T63/approx. 1.8° horizontal with 47 vertical levels) that covers the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009 ModE-Sim consists of 31620 simulated years in total. The dataset forms the input for a data assimilation procedure that combines historical climate informations with additional constraints from a climate model to produce a novel gridded 3-dimensional dataset of the modern era. Additionally, ModE-Sim on its own is also suitable for many other applications as its various subsets can be used as initial condition and boundary condition ensemble to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation of the two key variables 2m-temperature and precipitation. We demonstrate ModE-Sims ability to represent their mean state, to produce a reasonable response to external forcings and to sample internal variability. At the example of heat waves we show that the ensemble is even capable of capturing extreme events. [ABSTRACT FROM AUTHOR]

Sea ice is an important and complex component of the Earth system and is considered a sensitive indicator of climate change. The seasonal sea ice cycle regulates the exchange of heat and salinity, altering the energy balance between high and low latitudes as well as the ocean and atmospheric circulation. The accurate representation of Antarctic sea ice has been considered a hot topic in the climate modelling community and lacks conclusive answers. In this paper, we evaluated the ability of 11 climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) to simulate the sea ice seasonal cycle in Antarctica in terms of area (SIA) and concentration (SIC), as well as the improvements in the most recent models' version, submitted to CMIP6. The results indicated that all models are able to accurately capture the seasonal cycle of the Antarctic SIA, with the minimum (maximum) occurring in February (September). In the Weddell Sea, Amundsen Sea, Bellingshausen Sea, and the Ross Sea, the simulated sea ice concentration revealed a large and systematic bias in February when compared to observations. In September, a large and systematic bias was found nearby the Southern Ocean's northern limit in the Polar Front. Several CMIP6 models exhibited slight improvements on the SIA and SIC estimate over the previous version (CMIP5). All models indicated a significant sea ice loss in the coming years as a response to CO2 forcing. Despite the advancements in the sea ice representation, our findings show that the models are still unable to accurately represent the regional sea ice changes [ABSTRACT FROM AUTHOR]

Research into weather circulation changes over the UK for future climate has mainly focused on changes in the Summer and Winter seasons, with less analysis on seasonality and the transition seasons. Using the 30 Met Office weather patterns we examine the influence of climate change on seasonality through atmospheric circulation using a number of climate models. Changes in seasonality are important as they can have large impacts on many sectors including agriculture, energy and tourism. This paper finds a noticeable increase in Autumn over the UK in the frequency of drier summer-type regimes and a decrease in stormy winter types that emerge as early as the 2020s. The change in circulation signal once isolated from the overall signal is responsible for a 4–12% decrease in Autumn mean rainfall on average for England by the end of this century (where the values in the range are dependent on the emissions scenario). This change is projected over English regions that are already experiencing water stress, and with predictions of drier summers over the UK in future, this could further increase drought risk. The change in circulation in Autumn also moderates the large increase in the number of large-scale extreme daily rainfall events over the same regions predicted due to climate change. While this future circulation change is replicated across all the climate models used, large differences remain in the strength of the signal between models. The climate models used replicate the frequency of the 30 weather patterns well for all seasons. [ABSTRACT FROM AUTHOR]

This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress. Plain Language Summary: This paper describes the new global 3.25 km version of the Energy Exascale Earth System Model (E3SM) atmosphere model and its behavior in a 40‐day northern‐hemisphere wintertime simulation. In exchange for huge computational expense, this high‐resolution model avoids many but not all biases common in lower‐resolution models. It also captures several types of extreme weather that would simply not be resolved in lower‐resolution models. Several opportunities for further development are identified. Key Points: Describes the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM)SCREAM performs well in a 40‐day boreal winter simulation at 3.25 km ΔxResolving deep convection solves many long‐standing climate model biases [ABSTRACT FROM AUTHOR]

A number of recent papers have examined sea level data, both local tide gauge records and regional/global averages, to estimate not only how fast sea level is rising but how the rate has changed over time, i.e. its pattern of acceleration and deceleration. In addition, a number of claims of cyclic/quasi-periodic variations have been proposed. However, many of these papers contain technical problems which call their results into question. In particular, the issue of autocorrelation is often ignored, and even when it is addressed its impact has sometimes been misinterpreted. Autocorrelation does more than just affect the standard errors of regression analysis, it can also make the spectrum of a noise process distinctly 'red' and therefore be highly suggestive of low-frequency periodic or pseudo-periodic behavior when none is present. If any analysis is applied which acts as a band-pass filter, it can further exaggerate the illusion of oscillatory behavior. These issues are highlighted in a small number of recent papers, in order to improve the quality of future work on this subject. [ABSTRACT FROM AUTHOR]

Since the early 1990s the Pacific Walker circulation shows a multi‐decadal strengthening, which contradicts future model projections. Whether this trend, evident in many climate indices especially before the 2015 El Niño, reflects the coupled ocean‐atmosphere response to global warming or the negative phase of the Pacific Decadal Oscillation (PDO) remains debated. Here we show that sea surface temperature trends during 1980–2020 are dominated by three signals: a spatially uniform warming trend, a negative PDO pattern, and a Northern Hemisphere‐Indo‐West Pacific warming pattern. The latter pattern, which closely resembles the transient ocean thermostat‐like response to global warming emerging in a subset of CMIP6 models, shows cooling in the central‐eastern equatorial Pacific but warming in the western Pacific and tropical Indian Ocean. Together with the PDO, this pattern drives the Walker circulation strengthening in the equatorial band. Historical simulations appear to underestimate this pattern, contributing to the models' inability to replicate the Walker cell strengthening. Plain Language Summary: This paper investigates the observed changes in the tropical Pacific during the satellite era, including the recent decadal strengthening of the atmospheric zonal circulation—the Walker cell. We aim to understand the extent to which these changes represent a forced response to rising CO2 concentrations versus natural variability. We apply an approach in which we decompose the observed sea surface temperature trends into three components—a pattern associated with the Pacific Decadal Oscillation, which is part of natural variability, a uniform warming pattern, and a residual pattern. This residual pattern shows a remarkable resemblance to a forced ocean thermostat‐like transient response generated in some of the climate models, characterized by equatorial Pacific (EP) cooling, and a broad warming of the Northern Hemisphere, and the Indian Ocean and West Pacific. These results challenge studies arguing that the recent strengthening of the Pacific Walker cell can be explained simply by multi‐decadal natural variability in the tropics. Furthermore, the inability of climate models at large to fully capture this forced pattern with historical forcing puts into focus the reliability of future projections of climate change in the tropical Pacific, specifically the timing of emergence of the eastern EP warming. Key Points: A multi‐decadal strengthening of the Pacific Walker cell is observed in a wide range of indices, especially after 1990A Northern Hemisphere ‐ Indo West Pacific warming sea surface temperature pattern, which differs from the Pacific Decadal Oscillation, is evident since 1980This pattern resembles a forced response to abrupt CO2 forcing, emerging in a subset of climate models, and contributes to the Walker circulation strenthening [ABSTRACT FROM AUTHOR]

The earth's surface is heated by the large-scale movement of air known as atmospheric circulation, which works in conjunction with ocean circulation. More than 10 5 variables are involved in the complexity of the weather system. In this work, we analyze the dynamical behavior and chaos control of an atmospheric circulation model known as the Hadley circulation model, in the frame of Caputo and Caputo–Fabrizio fractional derivatives. The fundamental novelty of this paper is the application of the Caputo derivative with equal dimensionality to models that includes memory. A sliding mode controller (SMC) is developed to control chaos in this fractional-order atmospheric circulation system with uncertain dynamics. The proposed controller is applied to both commensurate and non-commensurate fractional-order systems. To demonstrate the intricacy of the models, we plot some graphs of various fractional orders with appropriate parameter values. We have observed the influence of thermal forcing on the dynamics of the system. The outcome of the analytical exercises is validated using numerical simulations. [ABSTRACT FROM AUTHOR]

Determining the underlying mechanisms leading to extreme events in dynamical systems is a challenging task. Under mild hypotheses, large deviations theory predicts that, as one increases the threshold defining an extreme, dynamical trajectories which reach the extreme look more and more like one another: they converge towards a typical, i.e. most probable, trajectory called the instanton. In this paper, we use a 2000-year simulation of the IPSL-CM6A-LR model under a stationary pre-industrial climate to test this prediction on the case of hot extremes. We investigate whether the physical mechanisms leading to extreme temperatures at four locations in Europe are more similar with increasing extreme temperatures. Our results show that most physical variables exhibit the expected convergence towards a most probable trajectory, with some geographical and temporal variations. In particular, we observe the presence of a cut-off low in some trajectories, which suggests the existence of multiple pathways leading to extreme temperatures. These findings confirm the relevance of instanton dynamics to understand the physical mechanisms driving extreme events in climate models. [ABSTRACT FROM AUTHOR]

During the first half of the Holocene (11,000 to 5,000 years ago) the Northern Hemisphere experienced a strengthening of the monsoonal regime, with climate reconstructions robustly suggesting a greening of the Sahara region. Paleoclimate archives also show that this so-called African Humid Period (AHP) was accompanied by changes in the climate conditions at mid to high latitudes. However, inconsistencies still exist in reconstructions of the mid-Holocene (MH) climate at mid-latitudes, and model simulations provide limited support to reduce these discrepancies. In this paper, a set of simulations performed with a climate model is used to investigate the hitherto unexplored impact of the Saharan greening on mid-latitude atmospheric circulation during the MH. Numerical simulations show a year-round impact of the Saharan greening on the main circulation features in the Northern Hemisphere, especially during boreal summer when the African monsoon develops. Key findings include a westward shift of the global Walker Circulation, leading to a modification of the North Atlantic jet stream in summer and the North Pacific jet stream in winter. Furthermore, the Saharan greening modifies the atmospheric synoptic circulation over the North Atlantic, transitioning the North Atlantic Oscillation phase from prevailingly positive to neutral-to-negative in winter and summer. This study provides a first constraint on the Saharan greening influence on northern midlatitudes, indicating new opportunities for understanding the MH climate anomalies in regions such as North America and Eurasia. [ABSTRACT FROM AUTHOR]

Coupled atmosphere‐fire modeling is recognized as a relevant approach for the representation of the interaction between a wildland fire and local meteorology at landscape scales. The atmospheric model component used in the coupled system is based on several approximations, which are adopted for computational efficiency or physical processes representation, including the widely used anelastic approximation. The validity domain of the anelastic approximation may be questioned in the context of high‐resolution wildland fire modeling due to the large fire‐induced heat releases near the surface. This study aims to study this question with the MesoNH anelastic model coupled with the Blaze fire model. A compressible version of the MesoNH‐Blaze coupled model has been developed for comparison with the anelastic system. The FireFlux I experimental fire is used for this comparative study conducted at a 10‐m and a 25‐m horizontal atmospheric resolution. Results show significant anelastic/compressible differences at a 10‐m resolution on the physical processes occurring near the fire with higher horizontal velocities and the presence of gravity waves downstream of the fire. This is in addition to the fire plume with realistic larger vertical velocities. Differences at a 25‐m resolution are much smaller in all evaluated processes. The compressible system only enriches the physics underlying fire‐atmosphere interactions at a very high resolution, which means that the anelastic approximation remains relevant for large‐scale coupled atmosphere‐fire simulations, considering the significant economy concerning numerical costs. Plain Language Summary: Wildfires burn large amounts of forests each year, destroying the living habitat of many species, endangering human settlements and provoking cross‐continent smoke events leading to air quality issues. Wildfires result from complex physical, chemical and biological processes. Understanding the fundamental processes driving wildfire behavior is a key point for the prediction of fire spread across the landscape and the induced atmospheric dynamics. Numerical models coupling the atmospheric dynamics (wind, temperature, air density and pressure, etc.) and fire front evolution are efficient tools in this scientific process. This paper evaluates how such model simulations are affected by the way in which the effects of high air density gradients induced by the very large amount of heat released into the atmosphere are modeled. When the spatial resolution of the atmospheric model is as high as 10 m, an exact – compressible – formulation leads to the formation of gravity waves downstream of the fire, and to stronger fire‐induced wind, fire propagation and larger vertical velocities than an approximate (and computationally faster) approach. At a resolution of 25 m, which is still very high, the approximate approach leads to results, which are as good as the exact one. This can then be used to simulate wildland fire behavior at landscape‐to‐meteorological scales. It also paves the way for future accurate wildfire forecast systems. Key Points: A compressible version of the MesoNH atmospheric model has been developed to evaluate equation system approximations in wildfire simulationHorizontally buoyancy‐driven effects induce turbulent movements, waves ahead of the fire, and a bending of the plumeThe anelastic approximation for the atmosphere model is suitable to represent wildland fire effects, except for resolutions of 10 m or finer [ABSTRACT FROM AUTHOR]

Atmospheric radiative cooling is a fundamental aspect of Earth's greenhouse effect, and is intrinsically connected to atmospheric motions. At the same time, basic aspects of longwave radiative cooling, such as its characteristic value of 2 K day−1, its sharp decline (or "kink") in the upper troposphere, and the large values of CO2 cooling in the stratosphere, are difficult to understand intuitively or estimate with pencil and paper. Here we pursue such understanding by building simple spectral (rather than gray) models for clear-sky radiative cooling. We construct these models by combining the cooling-to-space approximation with simplified greenhouse gas spectroscopy and analytical expressions for optical depth, and we validate these simple models with line-by-line calculations. We find that cooling rates can be expressed as a product of the Planck function, a vertical emissivity gradient, and a characteristic spectral width derived from our simplified spectroscopy. This expression allows for a pencil-and-paper estimate of the 2 K day−1 tropospheric cooling rate, as well as an explanation of enhanced CO2 cooling rates in the stratosphere. We also link the upper-tropospheric kink in radiative cooling to the distribution of H2O absorption coefficients, and from this derive an analytical expression for the kink temperature Tkink ≈ 220 K. A further, ancillary result is that gray models fail to reproduce basic features of atmospheric radiative cooling. [ABSTRACT FROM AUTHOR]

A critical stage in the development of our ability to model and project climate change occurred in the late 1950s–early 1960s when the first primitive-equation atmospheric general circulation models (AGCMs) were created. A rather idiosyncratic project to develop an AGCM was conducted virtually alone by Cecil E. Leith starting near the end of the 1950s. The Leith atmospheric model (LAM) appears to have been the first primitive-equation AGCM with a hydrological cycle and the first with a vertical resolution extending above the tropopause. It was certainly the first AGCM with a diurnal cycle, the first with prognostic clouds, and the first to be used as the basis for computer animations of the results. The LAM project was abandoned in approximately 1965, and it left almost no trace in the journal literature. Remarkably, the recent internet posting of a half-century-old computer animation of LAM-simulated fields represents the first significant "publication" of results from this model. This paper summarizes what is known about the history of the LAM based on the limited published articles and reports as well as transcripts of interviews with Leith and others conducted in the 1990s and later. [ABSTRACT FROM AUTHOR]

The topic of the paper is modelling and prediction of atmospheric variables that are further used for prediction of the consequences of radioactive-material release to the atmosphere. Physics-based models of atmospheric dynamics provide an approximate description of the true nature of a dynamic system. However, the accuracy of the model's short-term predictions and long-term forecasts, especially over complex terrain, decreases when the information at a micro-location is sought. Integration of a physics-based model with a statistical model for enhancing the prediction power is proposed in the paper. Gaussian Processes models can be used to identify the mapping between the system input and output measured values. With the given mapping function, we can provide one-step ahead prediction of the system output values together with its uncertainty, which can be used advantageously. In this paper, we combine a physics-based model with a Gaussian-process model to identify air temperature from measurements at different atmospheric surface layers as a dynamic system and to make short-term predictions as well as long-term forecasts. [ABSTRACT FROM AUTHOR]

Interdecadal variations of the relationship between El Niño–Southern Oscillation (ENSO) and the Indo-China Peninsula (ICP) surface air temperature (SAT) in winter are investigated in the study. Generally, there exists a positive correlation between them during 1958–2015 because the ENSO-induced anomalous western North Pacific anticyclone (WNPAC) is conducive to pronounced temperature advection anomalies over the ICP. However, such correlation is unstable in time, having experienced a high-to-low transition around the mid-1970s and a recovery since the early 1990s. This oscillating relationship is owing to the anomalous WNPAC intensity in different decades. During the epoch of high correlation, the anomalous WNPAC and associated southwesterly winds over the ICP are stronger, which brings amounts of warm temperature advection and markedly heats the ICP. In contrast, a weaker WNPAC anomaly and insignificant ICP SAT anomalies are the circumstances for the epoch of low correlation. It is also found that substantial southwesterly wind anomalies over the ICP related to the anomalous WNPAC occur only when large sea surface temperature (SST) anomalies over the northwest Indian Ocean (NWIO) coincide with ENSO (viz., when the ENSO–NWIO SST connection is strong). The NWIO SST anomalies are capable of driving favorable atmospheric circulation that effectively alters ICP SAT and efficiently modulates the ENSO–ICP SAT correlation, which is further supported by numerical simulations utilizing the Community Atmospheric Model, version 4 (CAM4). This paper emphasizes the non-stationarity of the ENSO–ICP SAT relationship and also uncovers the underlying modulation factors, which has important implications for the seasonal prediction of the ICP temperature. [ABSTRACT FROM AUTHOR]

This paper presents the results of the ensemble Riemannian data assimilation for relatively high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic (QG) model of atmospheric circulation. The analysis state in this approach is inferred from a joint distribution that optimally couples the background probability distribution and the likelihood function, enabling formal treatment of systematic biases without any Gaussian assumptions. Despite the risk of the curse of dimensionality in the computation of the coupling distribution, comparisons with the classic implementation of the particle filter and the stochastic ensemble Kalman filter demonstrate that, with the same ensemble size, the presented methodology could improve the predictability of dynamical systems. In particular, under systematic errors, the root mean squared error of the analysis state can be reduced by 20 % (30 %) in the Lorenz-96 (QG) model. [ABSTRACT FROM AUTHOR]

Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [ABSTRACT FROM AUTHOR]

This paper describes the development and evaluation of a new nonhydrostatic dynamical framework for global and regional atmospheric modeling, with an emphasis on the numerical performance of dry dynamics. The model is formulated in a layer‐averaged manner using a generalized hybrid sigma‐mass vertical coordinate and an unstructured mesh. The mass‐based equations allow a flexible and effective switch between the hydrostatic and nonhydrostatic solvers. The unstructured mesh treats the conventional icosahedral grid and the more general Voronoi polygon in a consistent manner, allowing a flexible switch between quasi‐uniform and variable‐resolution modeling. The horizontal discretization and vertical discretization are formulated in an explicit Eulerian approach, while those terms describing the vertically propagating fast waves are solved implicitly. The model is equipped with physically based Smagorinsky diffusion as a tuning tool. A suite of multiscale test cases from hydrostatic to nonhydrostatic regimes is used to assess the model performance. The general strategies for evaluation focus on two aspects: (i) the nonhydrostatic solver should behave similarly to its hydrostatic counterpart under the hydrostatic regime and (ii) the nonhydrostatic solver should produce unique nonhydrostatic responses under the nonhydrostatic regime. In the context of model evaluation, model sensitivity to numerical configurations is further explored to understand the impact of isolated components, helping to identify appropriate configurations for realistic modeling applications. The present framework is a prototype toward a Global‐Regional Integrated forecast SysTem (GRIST). Plain Language Summary: A dynamical framework describes the evolution of resolved‐scale fluid dynamical processes in a numerical model. It also helps to define model architecture, and its establishment usually initiates the development of a new model. This paper describes the development and evaluation of a new nonhydrostatic dynamical framework, which may be used for global and regional atmospheric modeling applications such as weather forecasting, climate prediction, and basic research. The model is based on a generalized vertical coordinate and an unstructured mesh. It is discretized in a fully explicit Eulerian approach, except for the vertically propagating fast waves. Idealized experimental protocols are used to demonstrate the performance of dry dynamics associated with atmospheric transport and wave‐like phenomena. Sensitivity tests are performed to understand the impact of numerical configurations on the model behavior. Key Points: A layer‐averaged nonhydrostatic dynamical framework on an unstructured mesh is developed for global and regional atmospheric modelingThe baseline numerical performance of dry dynamics is evaluatedThe sensitivity associated with the numerical configuration is explored [ABSTRACT FROM AUTHOR]

This paper evaluates the simulation performance of the 37 coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) with respect to the East Asian summer monsoon (EASM) and the Indo-Pacific warm pool and North Pacific Ocean dipole (IPOD) and also the interrelationships between them. The results show that the majority of the models are unable to accurately simulate the interannual variability and long-term trends of the EASM, and their simulations of the temporal and spatial variations of the IPOD are also limited. Further analysis showed that the correlation coefficients between the simulated and observed EASM index (EASMI) is proportional to those between the simulated and observed IPOD index (IPODI); that is, if the models have skills to simulate one of them then they will likely generate good simulations of another. Based on the above relationship, this paper proposes a conditional multi-model ensemble method (CMME) that eliminates those models without capability to simulate the IPOD and EASM when calculating the multi-model ensemble (MME). The analysis shows that, compared with the MME, this CMME method can significantly improve the simulations of the spatial and temporal variations of both the IPOD and EASM as well as their interrelationship, suggesting the potential for the CMME approach to be used in place of the MME method. [ABSTRACT FROM AUTHOR]

Evaluating land surface models (LSMs) using available observations is important for understanding the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a LSM, five essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, surface albedo, and precipitation) via simulations with the IPSL (Institute Pierre Simon Laplace) LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) model, particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH Forcing Data ERA-Interim – WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation, using satellite- and ground-based reference data, show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape and meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing and/or snowmelt, temperate plant phenology, irrigation, and contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in the coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy limited or water limited) and snow albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model. [ABSTRACT FROM AUTHOR]

Californian hydroclimate is strongly seasonal and prone to severe water shortages. Recent changes in climate trends have induced shifts in seasonality, thus exacerbating droughts, wildfires, and adverse water shortage effects on the environment and economy. Previous studies have examined the timing of the seasonal cycle shifts mainly as temperature driven earlier onset of the spring season. In this paper, we address quantitative changes in the onset, amounts, and termination of the precipitation season over the past 6 decades, as well as the large‐scale atmospheric circulation underpinning the seasonal cycle changes. We discover that the onset of the rainy season has been progressively delayed since the 1960s, and as a result the precipitation season has become shorter and sharper in California. The progressively later onset of the rainy season is shown to be related to the summer circulation pattern extending into autumn across the North Pacific, in particular, a delay in the strengthening of the Aleutian Low and later southward displacement of the North Pacific westerlies. Plain Language Summary: The rainy season over California is projected to show a distinct sharpening of the mean seasonal cycle, with winter precipitation increasing, and both autumn and spring precipitation decreasing. Our analysis of the past 6 decades of data for California suggests autumn decrease is already underway. A delayed start of the rainy season of 27 days since 1960s can exacerbate seasonal droughts and prolong the wildfire season. This delay occurs due to a number of conditions that controls precipitation: the summer circulation pattern has been extending throughout November across the North Pacific, and the wintertime strengthening of the Aleutian Low is delayed. Accordingly, the southward migration of the North Pacific jet stream as well as extratropical storm tracks are delayed, which marks the start of the California rainy season, are delayed. More work, using climate models, will be needed to provide a better understanding of atmospheric conditions across the North America and the North Pacific. However, our findings provide observational evidence for the projected rainfall change over California and inform ongoing discussion about the drying/wetting tendencies of the rainy season in California. Key Points: Rainfall data over the last 6 decades suggests a progressively delayed onset to the rainy season over CaliforniaA corresponding delay occurs in the transition from summer to wintertime circulation over the North PacificObserved autumn trends appear consistent with projected future shortening of the California rainy season [ABSTRACT FROM AUTHOR]

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a superparameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s−1. In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled. [ABSTRACT FROM AUTHOR]

This study assesses the benefit of convective‐scale data assimilation (DA) for model initialization using well‐known functional relationships between lightning flash rate and vertical velocity. Based on the relationships, a lightning DA scheme to update model kinematic states was implemented in the Weather Research and Forecasting Data Assimilation (WRFDA) three‐dimensional variational (3DVar) system. This scheme combines total lightning observations with model‐based prescribed vertical velocity profiles to retrieve kinematic information through a DA scheme. With the availability of space‐borne lightning imagers in recent years, total lightning observations from the Lightning Mapping Imager (LMI) on board the FY‐4A geostationary satellite were assimilated in combination with radar DA. A detailed analysis of the impact of the lightning DA scheme on convective precipitation forecasting was conducted using a squall line case over Beijing on 13 July 2017. The assimilation of LMI data provides added benefits to the assimilation of radar radial winds by reducing wind errors and strengthening convergence along the squall line in the analysis. Although the microphysical states are identical due to the assimilation of reflectivity, the lightning DA scheme helps in promoting updraft developments at lightning observation locations, which improves the representation of mixed‐phase convection. The quantitative verification of short‐term convective forecasts indicated that the lightning DA adds value to the radar DA by improving the precipitation forecast skill. The lightning DA scheme was evaluated further for a heavy rainfall case in 2018 over the Beijing metropolitan area and revealed overall similar forecast improvements for composite reflectivity and accumulated rainfall. Plain Language Summary: Lightning flashes are closely related to the upward air motions in thunderstorms and hence are indicative of strong wind convergence. Currently, lightning imagers on board the geostationary satellites provide increased availability of lightning data over broad regions and can improve weather forecasting accuracy. This paper describes how the space‐borne lightning observations could be employed to update model kinematic states and improve convective precipitation forecasting. Key Points: A lightning data assimilation (DA) scheme to update model kinematic states was developed using a three‐dimensional variational (3DVar) systemThe event data from Lightning Mapping Imager (LMI) aboard the FY‐4A geostationary satellite were assimilated to reflect lightning horizontal dimensionThe new lightning DA scheme improves the convective analysis and storm forecasting in two severe convective cases [ABSTRACT FROM AUTHOR]

Recently, much attention has been devoted to better understand the internal modes of variability of the climate system. This is particularly important in mid-latitude regions like the North-Atlantic, which is characterized by a large natural variability and is intrinsically difficult to predict. A suitable framework for studying the modes of variability of the atmospheric circulation is to look for recurrent patterns, commonly referred to as Weather Regimes. Each regime is characterized by a specific large-scale atmospheric circulation pattern, thus influencing regional weather and extremes over Europe. The focus of the present paper is the study of the Euro-Atlantic wintertime Weather Regimes in the climate models participating to the PRIMAVERA project. We analyse here the set of coupled historical simulations (hist-1950), which have been performed both at standard and increased resolution, following the HighresMIP protocol. The models' performance in reproducing the observed Weather Regimes is assessed in terms of different metrics, focussing on systematic biases and on the impact of resolution. We also analyse the connection of the Weather Regimes with the Jet Stream latitude and blocking frequency over the North-Atlantic sector. We find that—for most models—the regime patterns are better represented in the higher resolution version, for all regimes but the NAO-. On the other side, no clear impact of resolution is seen on the regime frequency of occurrence and persistence. Also, for most models, the regimes tend to be more tightly clustered in the increased resolution simulations, more closely resembling the observed ones. However, the horizontal resolution is not the only factor determining the model performance, and we find some evidence that biases in the SSTs and mean geopotential field might also play a role. [ABSTRACT FROM AUTHOR]

The Ocean–Land–Atmosphere Model (OLAM) has been developed to extend the capabilities of the Regional Atmospheric Modeling System (RAMS) to a global model domain. OLAM adopts many features of its predecessor, including physical parameterizations, initialization methods, data assimilation, logic and coding structure, and I/O formats. However, its dynamic core is new and is based on a global geodesic grid with triangular mesh cells and a finite-volume discretization of the full compressible Navier–Stokes equations. Local mesh refinement can be activated in OLAM to cover selected geographic areas at very high resolution and hence to simulate atmospheric systems typically studied in regional models. This paper is one in a series whose purpose is to describe the formulation of OLAM and to validate the model’s performance through test results. This paper focuses on global-scale dynamics that can be represented by the shallow-water equations, and provides details of the spatial and temporal discretizations, which contain some unique algorithms that previously have not been applied to atmospheric models. Validation tests are performed using five standard shallow-water simulations that are commonly used benchmarks for global models. OLAM results for all tests are comparable to results from other global models. [ABSTRACT FROM AUTHOR]

We have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model (SP‐CAM). The new configuration, called the multiple‐instance SP‐CAM, or MP‐CAM, uses the average heating and drying rates from 10 independent two‐dimensional cloud‐permitting models (CPMs) in each grid column of the global model, instead of a single CPM. The 10 CPMs start from slightly different initial conditions and simulate alternative realizations of the convective cloud systems. By analyzing the ensemble of possible realizations, we can study the predictability of the cloud systems and identify the weather regimes and physical mechanisms associated with chaotic convection. We explore alternative methods for quantifying the predictability of precipitation. Our results show that unpredictable precipitation occurs when the simulated atmospheric state is close to critical points as defined by Peters and Neelin (2006, https://doi.org/10.1038/nphys314). The predictability of precipitation is also influenced by the convective available potential energy and the degree of mesoscale organization. It is strongly controlled by the large‐scale circulation. A companion paper compares the global atmospheric circulations simulated by SP‐CAM and MP‐CAM. Key Points: Multiple‐instance superparameterization is described, and its small‐scale ensemble is used to explore precipitation predictabilityA measure of spread, called proportional variability, is found to reliably characterize predictabilityUnpredictable precipitation is related to the CAPE magnitude, degree of mesoscale organization, and proximity to atmospheric critical points [ABSTRACT FROM AUTHOR]

We assess the reliability of an indirect method of inferring the atmospheric response to projected Arctic sea ice loss from CMIP5 simulations, by comparing the response inferred from the indirect method to that explicitly simulated in sea ice perturbation experiments. We find that the indirect approach works well in winter, but has limited utility in the other seasons. We then apply a modified version of the indirect method to 11 CMIP5 models to reveal the robust and non‐robust aspects of the wintertime atmospheric response to projected Arctic sea ice loss. Despite limitations of the indirect method, we identify a robust enhancement of the Siberian High, weakening of the Icelandic Low, weakening of the westerly wind on the poleward flank of the eddy‐driven jet, strengthening of the subtropical jet, and weakening of the stratospheric polar vortex. The surface air temperature response to projected Arctic sea ice loss over mid‐latitude continents is non‐robust across the models. Plain Language Summary: The continued melt of Arctic sea ice will likely affect weather and climate in places far from the Arctic. To better understand the far‐flung implications of sea ice loss, scientists can perform bespoke climate model experiments in which sea ice is reduced, but all other climate drivers are fixed. In our paper, we test the reliability of an indirect method to infer the atmospheric response to sea ice loss, which makes use of a large set of climate model experiments originally performed for other purposes. We find that the indirect approach works well in winter, but not so well in other seasons. We then apply the indirect method to 11 climate models to reveal the robust and non‐robust aspects of the wintertime atmospheric response to projected Arctic sea ice loss—the largest such comparison to date. We found that the models agreed on quite a few aspects of the response, including warming over the Arctic, regional surface pressure changes over Siberia and Iceland, and a slowing of the mid‐latitude jet stream. On the other hand, the models disagreed on whether surface air temperatures over Europe, North America, and East Asia will warm or cool in response to future Arctic sea ice loss. Key Points: Wintertime atmospheric response to projected Arctic sea‐ice loss can be well‐estimated indirectly from CMIP5 simulationsRobust changes across models including stronger Siberian High, weaker Icelandic Low, slower eddy‐driven jet and faster subtropical jetNon‐robust changes in surface air temperature over mid‐latitude continents and sea level pressure over the North Pacific [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To meet the challenges posed by future generations of massively parallel supercomputers, a reformulation of the dynamical core for the Met Office's weather and climate model is presented. This new dynamical core uses explicit finite‐volume type discretizations for the transport of scalar fields coupled with an iterated‐implicit, mixed finite‐element discretization for all other terms. The target model aims to maintain the accuracy, stability and mimetic properties of the existing Met Office model independent of the chosen mesh while improving the conservation properties of the model. This paper details that proposed formulation and, as a first step towards complete testing, demonstrates its performance for a number of test cases in (the context of) a Cartesian domain. The new model is shown to produce similar results to both the existing semi‐implicit semi‐Lagrangian model used at the Met Office and other models in the literature on a range of bubble tests and orographically forced flows in two and three dimensions. [ABSTRACT FROM AUTHOR]

Models of the carbon cycle and climate on geologic (> 104 year) timescales have improved tremendously in the last 50 years due to parallel advances in our understanding of the Earth system and the increase in computing power to simulate its key processes. Despite these advances, balancing the Earth System's vast complexity with a model's computational expense is a primary challenge in model development. Running longer simulations spanning hundreds of thousands of years or more generally requires reducing the complexity of the modeled climate system. However, simpler model frameworks often leave out certain features of the climate system, such as radiative feedbacks, shifts in atmospheric circulation, and the expansion and decay of ice sheets, which can have profound effects on the long-term carbon cycle. Here, we present a model for climate and the long-term carbon cycle that captures many fundamental features of global climate while retaining the computational efficiency needed to simulate millions of years of time. The Carbon-H2O Coupled HydrOlOgical model with Terrestrial Runoff And INsolation, or CH2O-CHOO TRAIN, couples a one-dimensional (latitudinal) moist static energy balance model of climate with a model for rock weathering and the long-term carbon cycle. The key advantages of this framework are (1) it simulates fundamental climate forcings and feedbacks; (2) it accounts for geographic configuration; and (3) it is highly customizable, equipped to easily add features, change the strength of feedbacks, and prescribe conditions that are often hardcoded or emergent properties of more complex models, such as climate sensitivity and the strength of meridional heat transport. The CH2O-CHOO TRAIN is capable of running million-year-long simulations in about thirty minutes on a laptop PC. This paper outlines the model equations, presents a sensitivity analysis of the climate responses to varied climatic and carbon cycle perturbations, and discusses potential applications and next stops for the CH2O-CHOO TRAIN. [ABSTRACT FROM AUTHOR]

Convective processes are crucial in shaping exoplanetary atmospheres but are computationally expensive to simulate directly. A novel technique of simulating moist convection on tidally locked exoplanets is to use a global 3D model with a stretched mesh. This allows us to locally refine the model resolution to 4.7 km and resolve fine-scale convective processes without relying on parameterizations. We explore the impact of mesh stretching on the climate of a slowly rotating TRAPPIST-1e-like planet, assuming it is 1:1 tidally locked. In the stretched-mesh simulation with explicit convection, the climate is 5 K colder and 25% drier than that in the simulations with parameterized convection(with both stretched and quasi-uniform meshes). This is due to the increased cloud reflectivity—because of an increase in low-level cloudiness—and exacerbated by the diminished greenhouse effect due to less water vapor. At the same time, our stretched-mesh simulations reproduce the key characteristics of the global climate of tidally locked rocky exoplanets, without any noticeable numerical artifacts. Our methodology opens an exciting and computationally feasible avenue for improving our understanding of 3D mixing in exoplanetary atmospheres. Our study also demonstrates the feasibility of a global stretched-mesh configuration for LFRic-Atmosphere, the next-generation Met Office climate and weather model. [ABSTRACT FROM AUTHOR]

How global warming is impacting winter cold extremes is uncertain. Previous work has found decreasing winter temperature variability over North America which suggests a reduction in frequency and intensity of cold extremes relative to mean changes. However, others argue that cold air outbreaks are becoming more likely because of Arctic-induced changes in atmospheric circulation. Here we show that cold extremes over North America have warmed substantially faster than the winter mean temperature since 1980. This amplified warming is linked to both decreasing variance and changes in higher moments of the temperature distributions. Climate model simulations with historical forcings robustly capture the observed trends in extremes and variability. A pattern-based detection and attribution analysis shows that the changes in variability are detectable in observations and can be attributed to human influence. Our results highlight that human emissions are warming North American extreme cold temperatures beyond only shifting the winter mean temperature. This study shows that the coldest winter days over North America are warming faster than the seasonal mean temperatures. These trends are robustly captured by climate models, and they are linked to human-caused temperature variability changes. [ABSTRACT FROM AUTHOR]

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs. Plain Language Summary: Variations in the ocean surface temperature (SST) influence the atmospheric circulation and thus climate over land. Canonical understanding is that tropical SSTs are more important than SSTs in midlatitudes. However, this understanding is based on climate models that don't resolve processes at scales less than 100 km. Here, we show that by increasing the atmospheric model resolution to resolve features on smaller scales, such as weather fronts, we find a larger atmospheric circulation response to midlatitude SST anomalies in the North Atlantic. North Atlantic SST anomalies can be predicted multiple years in advance, and a larger atmospheric circulation response to these predictable SST anomalies therefore implies increased predictability of climate over the surrounding land regions. Key Points: There is a large circulation response to idealized Gulf Stream sea‐surface temperature (SST) anomalies in an atmospheric model with 14‐km regional grid refinementThis response is weaker or absent in simulations with 28‐km or coarser resolution, which do not fully resolve mesoscale frontal processesTransient‐eddy fluxes of heat and momentum are modified as fronts pass over warm SSTs, leading to a large‐scale circulation response [ABSTRACT FROM AUTHOR]