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Dynamical prediction of monsoon rainfall has been an important topic and a long-standing issue in both research and operational community. This paper provides a comprehensive evaluation of the subseasonal-to-seasonal (S2S) prediction skill of the East Asian summer monsoon (EASM) rainfall using the hindcast record from the Beijing Climate Center Climate System Model, BCC CSM1.1m, during the period 1983–2019. The model exhibits reasonable skills for predicting the EASM rainfall at all lead times with the skill dropping dramatically from the shortest lead time of about 2 weeks (LM0) to 1-month lead (LM1), and then fluctuating remarkably throughout 2-month to 12-month lead times. Over the EASM domain, the rapid decline of the S2S rainfall prediction skill from LM0 to LM1 is mainly caused by the inferior skills over Central China in July and over Northeast China in August. Composite analysis based on hindcast records suggest that these inferior skills are directly tied to the model's difficulties in capturing above-normal precipitation over eastern Central China and Northeast China in the respective months, which are further shown to be associated with anomalous weakening and meridional movement of the Northwestern Pacific subtropical high and the activity of large-scale teleconnection pattern hard to be predicted over northeastern Asia in summer, respectively. These findings inform the intrinsic limits of the S2S predictability of the EASM rainfall by a dynamical model, and strongly suggest that the level of confidence placed upon S2S forecasts should be stratified by large-scale circulation anomalies known to significantly affect the prediction skill, e.g., the subtropical high and high-latitude teleconnection patterns for summer monsoon rainfall prediction in this region. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the extreme Atlantic and Benguela Niño events that occurred during the boreal spring–summer of 2021. We conducted sensitivity experiments with a regional ocean–atmosphere coupled model of the tropical Atlantic to investigate the phenology of these interannual events, unravel their triggering mechanisms, and quantify the contributions of local and remote processes. The results revealed that both the 2021 Atlantic and Benguela Niños were triggered by anomalous atmospheric fluxes at the model southern boundary (32° S), leading to a significant and persistent weakening of the South Atlantic Anticyclone. The associated poleward anomalous coastal wind off Africa reduced coastal upwelling and evaporation south of 15° S, initiating the Benguela Niño. Then, the relaxation of the equatorial trade winds forced a downwelling equatorial Kelvin wave, which warmed the eastern equatorial region, marking the onset of the Atlantic Niño. The equatorial event reached full maturity in July 2021 through ENSO-like air-sea interactions in the equatorial basin, enhanced by the atmospheric connection associated with low-level winds converging toward the distant coastal warming. While air–sea interactions in the tropical Atlantic acted as a negative feedback for the coastal warming, the ocean connection with the equatorial variability through the propagation of equatorially-forced downwelling coastal waves intensified the coastal warming, peaking end of May 2021. Overall, this research provides valuable insights into the complex dynamics of Atlantic and Benguela Niños, emphasizing the interconnectedness between these two systems. This has important implications for improving Earth system models which currently struggle to simulate these extreme events. [ABSTRACT FROM AUTHOR]

This paper investigates the process behind climate model biases, especially in rainfall, during the southwest (June to August; JJA) and northeast monsoon (December to February; DJF) seasons over the Philippines using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). Biases in the surface and near-surface climate are assessed over land in eastern (EastPH) and western (WestPH) Philippines using observation and reanalysis datasets. Results show that models with well-represented sea surface temperature (SST) provided better added values after downscaling. Although the CMIP5-driven models tend to overestimate both surface energy and water partitioning, simulations with well-represented SST have better surface energy partitions. In contrast to the reference simulation driven by ERA-Interim reanalysis, the CMIP5-driven simulations tend to underestimate (overestimate) moisture flux convergence during the DJF (JJA) season, which might have contributed to the overall underestimation (overestimation) of rainfall in this season. Further investigation along a vertical transect shows the differences in governing mechanisms for each season. During DJF (JJA) season, the simulation with underestimated (overestimated) SST, near-surface air temperature, and mixing ratio results in underestimated (overestimated) rainfall over EastPH (WestPH). In addition, the CMIP5-driven models were found to overestimate low-level winds, which may have induced the deep convection regardless of season, suggesting the role of low-level winds in the convection scheme used in RegCM4 aside from topography and land cover. [ABSTRACT FROM AUTHOR]

High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere–ocean model simulations, an assimilative coupled atmosphere–ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514–12522, 2018. https://doi.org/10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2–100 days) that are still relatively "high-frequency" in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the "drizzle effect", in which precipitation is not temporally intermittent to the extent found in observations. [ABSTRACT FROM AUTHOR]

An Emergent Constraint (EC) is a physically-explainable relationship between model simulations of a past climate variable (predictor) and projections of a future climate variable (predictand). If a significant correlation exists between the predictand and the predictor, observations of the latter can be used to constrain model projections of the former and to narrow their uncertainties. In the present study, the EC technique has been applied to the analysis of precipitation, one of the variables most affected by model uncertainties and still insufficiently analysed in the context of ECs, particularly for the recent CMIP6 model ensemble. The main challenge in determining an EC is establishing if the relationship found is physically meaningful and robust to the composition of the model ensemble. Four precipitation ECs already documented in the literature and so far tested only with CMIP3/CMIP5, three of them involving the analysis of extreme precipitation, have been reconsidered in this paper. Their existence and robustness are evaluated using different subsets of CMIP5 and CMIP6 models, verifying if the EC is still present in the most recent ensemble and assessing its sensitivity to the detailed ensemble composition. Most ECs considered do not pass this test: we found one EC not to be robust in both CMIP5 and CMIP6, other two exist and are robust in CMIP5 but not in CMIP6, and only one is verified and is robust in both model ensembles. [ABSTRACT FROM AUTHOR]

Meteorological processes over islands with complex orography could be better simulated by Convection Permitting Regional Climate Models (CP-RCMs) thanks to an improved representation of the orography, land–sea contrasts, the combination of coastal and orographic effects, and explicit deep convection. This paper evaluates the ability of the CP-RCM CNRM-AROME (2.5-km horizontal resolution) to simulate relevant meteorological characteristics of the Mediterranean island of Corsica for the 2000–2018 period. These hindcast simulations are compared to their driving Regional Climate Model (RCM) CNRM-ALADIN (12.5-km horizontal resolution and parameterised convection), weather stations for precipitation and wind and gridded precipitation datasets. The main benefits are found in the representation of (i) precipitation extremes resulting mainly from mesoscale convective systems affected by steep mountains during autumn and (ii) the formation of convection through thermally induced diurnal circulations and their interaction with the orography during summer. Simulations of hourly precipitation extremes, the diurnal cycle of precipitation, the distribution of precipitation intensities, the duration of precipitation events, and sea breezes are all improved in the 2.5-km simulations with respect to the RCM, confirming an added value. However, existing differences between model simulations and observations are difficult to explain as the main biases are related to the availability and quality of observations, particularly at high elevations. Overall, better results from the 2.5-km resolution, increase our confidence in CP-RCMs to investigate future climate projections for Corsica and islands with complex terrain. [ABSTRACT FROM AUTHOR]

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian region. [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]

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]

Characteristic adjustment time scale τ is an important parameter in convective parameterization scheme. In this paper, we propose a dynamic calculation formula of τ in the Zhang–McFarlane scheme based on convective available potential energy (CAPE) in order to improve the simulation of precipitation. The performance of using the new formula is tested and evaluated using the Community Atmosphere Model (CAM5) for numerical experiments. The results indicate that the root mean square errors of mean precipitation amount over the Northern Hemisphere eastern equatorial Pacific Ocean, the central Indo-Pacific Warm Pool, the Tibetan Plateau, and northern South America are reduced by 20.15, 16.40, 12.56, and 6.45% in summer, respectively, after a dynamic τ is adopted. The dynamically computed τ improves the Pacific Walker circulation by strengthening the western ascending and eastern descending, and increases the intensity of deep convective precipitation in tropical areas by at least 1 mm/day. Dynamic τ also increases the frequency of large deep convective precipitation in the equatorial Pacific by almost 50%, and decreases the frequency of light deep convective precipitation in many other regions. The dynamic τ has positive effects in the areas with large CAPE and spatiotemporal variability, and also leads to an increase in deep convective precipitation and a significant decrease in large-scale precipitation over high-altitude regions of windward slopes. Therefore, the generation of exaggerated precipitation in the highlands is reduced in the CAM5. Overall, the dynamic calculation of characteristic adjustment time scale can improve precipitation simulation. [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]

Regional climate projections indicate that European summer precipitation may change considerably in the future. Southern Europe can expect substantial drying while Northern Europe could actually become wetter. Model spread and internal variability in these projections are large, however, and unravelling the processes that underlie the changes is essential to get more confidence in these projections. Large-scale circulation change is one of the contributors to model spread. In this paper we quantify the role of future large-scale circulation changes to summer precipitation change, using a 16-member single-model ensemble obtained with the regional climate model RACMO2, forced by the global climate model EC-Earth2.3 and the RCP8.5 emission scenario. Using the method of circulation analogues three contributions to the future precipitation change are distinguished. The first is the precipitation change occurring without circulation change (referred to as the thermodynamic term). This contribution is characterised by a marked drying-to-wetting gradient as one moves north from the Mediterranean. The second contribution measures the effects of changes in the mean circulation. It has a very different spatial pattern and is closely related to the development of a region of high pressure (attaining its maximum west of Ireland) and the associated anti-cyclonic circulation response. For a large area east of Ireland including parts of western Europe, it is the major contributor to the overall drying signal, locally explaining more than 90% of the ensemble-mean change. In regions where the patterns overlap, the signal-to-noise ratio of the total change is either enhanced or reduced depending on their relative signs. Although the second term is expected to be particularly model dependent, the high-pressure region west of Ireland also appears in CMIP5 and CMIP6 ensemble-mean projections. The third contribution records the effects of changes in the circulation variability. This term has the smallest net contribution, but a relatively large uncertainty. The analogues are very good in partitioning the ensemble-mean precipitation change, but describe only up to 40% of the ensemble-spread. This demonstrates that other precipitation-drivers (SST, spring soil moisture etc.) will generally strongly influence trends in single climate realisations. This also re-emphasises the need for large ensembles or using alternative methods like the Pseudo Global Warming approach where signal to noise ratios are higher. Nevertheless, identifying the change mechanisms helps to understand the future uncertainties and differences between models. [ABSTRACT FROM AUTHOR]

Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity. [ABSTRACT FROM AUTHOR]

Climate change leads to a more frequent occurrence of hot days (HDs) and hot nights (HNs). The consecutive occurrence of HDs and HNs (COHs) is often used as a measure of the persistence of an extremely hot spell. Nonetheless, the combined effect of air temperature and relative humidity on the changing COHs has never been studied. In this paper, we use an ensemble of global climate models and multiple thermal indices to robustly examine the combined effect of air temperature and relative humidity on COHs globally on an hourly basis. Our findings reveal that COHs show an increasing trend in the future and a strong latitudinal gradient increasing from high latitudes to the equator. Compared to COHs based on air temperature, the frequency of COHs based on perceived temperature is amplified by the combined effects of high temperature and humidity for both boreal and austral summers. To investigate the underlying mechanisms, we examine two different diurnal temperature ranges (DTRs), derived from air temperature and perceived temperature, for their corresponding types of COHs. Both DTRs are projected to increase in the future relative to the historical period from 1980 to 2004, but the DTR changes derived from perceived temperature are consistently larger than those derived from air temperature. Due to the nonlinearity in thermal indices, the perceived temperature in HDs and HNs rising faster than air temperature leads to a larger increase in perceived COHs. The COHs are further amplified by the increasing number of HNs and HDs that occur consecutively under wet conditions. [ABSTRACT FROM AUTHOR]

Contributions to climate uncertainty in the modeling of the near surface temperature of the ocean come from several sources: the knowledge of the initial state of the atmosphere, the inadequacy of a model to include or correctly represent all aspects of the dynamics, the uncertain knowledge of the initial state of the ocean and small-scale (or internal, "weather") noise. This paper concentrates on the latter uncertainty, the uncertainty in the knowledge of the initial field, resulting in a map of the uncertainty of the near surface temperature field. To make the full estimate of this uncertainty, the outcomes from a relatively small, 30 member, ensemble were expanded using statistical emulators. The uncertainty varies in space and decreases in time. There are broad areas of similar uncertainty, consistent with uncertainties in the initial conditions and the variability in local processes. After 10 years, the average spatial uncertainty calculated from this expanded data set gives a range of +/− 0.4 ∘ C with 95% of the spatial areas having an uncertainty of less than +/− 0.9 ∘ C. When compared to other estimates of similar metrics, the map represents a lower bound on the uncertainty because this first experiment examines the ocean/ice system in isolation from feedbacks that occur between the atmosphere and the ocean. This uncertainty also does not include any contribution from the lack of knowledge about how a future state of the air/ocean system will be forced by changes in greenhouse gases. [ABSTRACT FROM AUTHOR]

Dynamical downscaling (DD) consists in using archives of Coupled Global Climate Models (CGCM) simulations as the atmospheric and sea-surface boundary conditions (BC) to drive nested, Regional Climate Model (RCM) simulations. Biases in the CGCM-generated driving BC, however, can have detrimental impacts on RCM performance. It is well documented for the historical period that CGCM-simulated sea-surface temperatures (SST) suffer substantial biases, especially important near coastal regions. Assuming that these SST biases are time-invariant, they could in principle be subtracted from century-long CGCM projections before being used to drive RCMs. This paper investigates the performance of a 3-step DD approach as follows. The CGCM-simulated sea-surface temperatures (SST) are first empirically corrected by subtracting their systematic biases; the corrected SST are then used as ocean surface BC for an atmosphere-only GCM (AGCM) simulation; finally this AGCM simulation provides the atmospheric lateral BC to drive an RCM simulation. This is what we refer to as the 3-step approach CGCM–AGCM–RCM of DD, which can be compared to the traditional 2-step approach CGCM–RCM consisting of driving an RCM simulation directly by CGCM-generated BC. In this paper we compare the results obtained with the two approaches, for present and future climates under RCP8.5, using the fifth-generation Canadian Regional Climate Model (CRCM5) with a grid mesh of 0.22° over the North American CORDEX domain, driven by two CMIP5 models: the Canadian Earth System Model of the Canadian Centre for Climate modelling and analysis (CanESM2) and the Earth System Model of the Max-Planck-Institut für Meteorologie (MPI-ESM-MR). The results show that, in current climate, the seasonal-mean 2-m temperature fields simulated with the 3-step DD have generally smaller biases with respect to the observations than those simulated with the 2-step DD; in fact the performance of the 3-step DD simulations often approaches that of the reanalyses-driven simulation. For the seasonal-mean precipitation field, however, the differences between the two DD methods are not conclusive. Differences between the projected climate changes with the two DD methods vary substantially depending upon the variable being considered. Differences are particularly important for temperature: over the bulk of the North American continent, the 3-step DD projects more warming in winter and less in summer. This result highlights the nonlinearities of the climate system, and constitutes an additional measure of uncertainty with DD. [ABSTRACT FROM AUTHOR]

It has been hypothesized that the ocean mesoscale (particularly ocean fronts) can affect the strength and location of the overlying extratropical atmospheric storm track. In this paper, we examine whether resolving ocean fronts in global climate models indeed leads to significant improvement in the simulated storm track, defined using low level meridional wind. Two main sets of experiments are used: (i) global climate model Community Earth System Model version 1 with non-eddy-resolving standard resolution or with ocean eddy-resolving resolution, and (ii) the same but with the GFDL Climate Model version 2. In case (i), it is found that higher ocean resolution leads to a reduction of a very warm sea surface temperature (SST) bias at the east coasts of the U.S. and Japan seen in standard resolution models. This in turn leads to a reduction of storm track strength near the coastlines, by up to 20%, and a better location of the storm track maxima, over the western boundary currents as observed. In case (ii), the change in absolute SST bias in these regions is less notable, and there are modest (10% or less) increases in surface storm track, and smaller changes in the free troposphere. In contrast, in the southern Indian Ocean, case (ii) shows most sensitivity to ocean resolution, and this coincides with a larger change in mean SST as ocean resolution is changed. Where the ocean resolution does make a difference, it consistently brings the storm track closer in appearance to that seen in ERA-Interim Reanalysis data. Overall, for the range of ocean model resolutions used here (1° versus 0.1°) we find that the differences in SST gradient have a small effect on the storm track strength whilst changes in absolute SST between experiments can have a larger effect. The latter affects the land-sea contrast, air-sea stability, surface latent heat flux, and the boundary layer baroclinicity in such a way as to reduce storm track activity adjacent to the western boundary in the N. Hemisphere storm tracks, but strengthens the storm track over the southern Indian Ocean. A note of caution is that the results are sensitive to the choice of storm track metric. The results are contrasted with those from a high resolution coupled simulation where the SST is smoothed for the purposes of computing air-sea fluxes, an alternative method of testing sensitivity to SST gradients. [ABSTRACT FROM AUTHOR]

Observational analysis and climate modeling efforts concur that the frequency, intensity, and duration of heatwaves will increase as the Earth's mean climate shifts towards warmer temperatures. While the impacts and mechanisms of heatwaves have been well explored, extreme temperatures over Florida are generally understudied. This paper sheds light on Floridian heatwaves by exploring 13 years of daily data from surface observations and high-resolution WRF climate simulations for the same timeframe. The characteristics of the current and future heatwaves under the RCP8.5 high emissions scenario for 2070-2099 were then investigated. Results show a tripling in the frequency, and greater than a sixfold increase in the mean duration of heatwaves over Florida when the current standard of heatwaves was used. The intensity of heatwaves also increased by 4-6 °C due to the combined effects of rising mean temperatures and a 1-2 °C increase attributed to the flattening of the temperature distribution. Since Florida's atmospheric boundary layer is rich in moisture and heatwaves could further increase the moisture content in the lower troposphere, the relationship between heatwaves and extreme precipitation was also explored in both the current and future climate. As expected, rainfall during a heatwave event was anomalously low, but it quickly recovered to normal within 3 days after the passage of a heatwave. Finally, the late 21st-century climate could witness a slight decrease in the mean precipitation over Florida, accompanied by heavier heatwave-associated extreme precipitation events over central and southern Florida. [ABSTRACT FROM AUTHOR]

This paper compiles indices for the El Niño—Southern Oscillation (ENSO) and seven unforced decadal-to-multidecadal climate modes (Interdecadal Pacific Oscillation (IPO), Tripole Pacific Index (TPI), Pacific Decadal Oscillation focused over North Pacific (PDO) and South Pacific (SPDO), North Atlantic Multidecadal Oscillation (AMO), South Atlantic Multidecadal Oscillation (SAMO), and Indian Ocean basin (IOB)) in a 100-member ensemble of the Max Planck Institute Earth System Model (MPI-ESM1.1) and a 35-member ensemble of Community Earth System Model (CESM1). Comparison among various detrending approaches indicates that the best approach to remove the unforced component is by subtracting the ensemble average temperature at each grid point from the original model output. Similar characteristics are investigated in the indices of both MPI-ESM1.1 and CESM1 ensemble models. The results further indicate no statistically significant lead-lag correlations between the unforced multidecadal climate modes originating from North Pacific (e.g., PDO) and North Atlantic (e.g., AMO), suggesting that a high correlation found in previous observational studies may be due to biased detrending approaches. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are important warm-season precipitation systems in eastern China. However, our knowledge of their climatology and capability in their simulation is still insufficient. This paper examines their characteristics over the 2008–2017 warm seasons using convection-permitting climate simulations (CPCSs) with a 3-km grid spacing that explicitly resolves MCSs, as well as a high-resolution gauge-satellite merged precipitation product. An object-based tracking algorithm is applied to identify MCSs. Results indicate that the MCS genesis and occurrence are closely related to the progression of the East Asian monsoon and are modulated by the underlying topography. On average, about 243 MCSs are observed each season and contribute 19% and 47% to total and extreme warm-season precipitation. The climatological attributes and variabilities are reasonably reproduced in the CPCS. The major model deficiencies are excessive small MCS occurrence and overmuch MCS rainfall, consequently overestimating the precipitation contributions, whereas observational uncertainties may play a role too. Both the observed and simulated MCS precipitation feature a nocturnal or morning maximum and an eastward delayed diurnal peak east of the Tibetan Plateau, in contrast to the dominant afternoon peak of non-MCS precipitation. The favorable comparison with observations demonstrates the capability of CPCSs in simulating MCSs in the Asian monsoon climate, and its usefulness in projecting the future changes of MCSs under global warming. The finding that non-MCS precipitation is responsible for the high biased afternoon precipitation provides helpful guidance for further model improvement. [ABSTRACT FROM AUTHOR]

In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale 'perfect' reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of "black-box" models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs. [ABSTRACT FROM AUTHOR]

10.1007/s00382-020-0539-w 21 Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P. Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. All these issue are currently under discussion within the CORDEX community, so that the CORDEX-CORE initiative can remain a central resource for the climate modeling and climate service communities. The CORDEX-CORE EXP-I dataset is indeed intended to be a community resource for the study of regional climate phenomena in a climate change context and for application to vulnerability-impacts-adaptation (VIA) assessment studies and climate service activities. The Coordinated Regional Downscaling Experiment (CORDEX, Giorgi et al. [15]; Jones et al. [24]) is the main reference program for the climate downscaling community. [Extracted from the article]

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets. [ABSTRACT FROM AUTHOR]

Climate models in the past decades have been developed to such an extent to include atmospheric chemistry as part of their climate simulations. This is necessary for providing policy-makers and other stakeholders with reliable atmospheric projections as well as information about changes in chemical species as a consequence of climate change. The regional climate model (RCM), RegCM4 is a community model which contains only one gas-phase mechanism module (CBM-Z) that includes the formation, deposition, and transport of a number of volatile organic compounds (VOCs). In this paper, the CB6-C, a new gas-phase mechanism module, is combined with RegCM4 to produce a larger suite of VOCs and chemical mechanisms for important anthropogenic and biogenic species, most notably benzene, terpenes, acetylene and their corresponding oxidation products. In order to evaluate this new module, climatologies of the CB6-C and CBM-Z simulations are compared to gridded and station data. The results reveal the two schemes to be similar with some improvement of surface carbon monoxide and tropospheric ozone in the CB6-C. However, organic products were found to be under-predicted for both schemes, suggesting the need of more development in the implementation of atmospheric chemistry in RegCM4. Despite its limitations, the input conditions (emissions and boundary conditions) are easy to modify, making the new gas-phase scheme an important advancement in the modelling of atmospheric chemistry within a RCM, as it provides a pathway for new research that may eventually help health studies. [ABSTRACT FROM AUTHOR]

This paper presents an inter-comparison performance assessment of the newly developed Centre for Weather Forecast and Climate Studies (CPTEC) model (the Brazilian Atmospheric Model version 1.2, BAM-1.2) against four sub-seasonal to seasonal (S2S) prediction project models from: Japan Meteorological Agency (JMA), Environmental and Climate Change Canada (ECCC), European Centre for Medium-range Weather Forecasts (ECMWF) and Australian Bureau of Meteorology (BoM). The inter-comparison was performed using hindcasts of weekly precipitation anomalies and the daily evolution of Madden–Julian Oscillation (MJO) for 12 extended austral summers (November–March, 1999/2000–2010/2011), leading to a verification sample of 120 hindcasts. The deterministic assessment of the prediction of precipitation anomalies revealed ECMWF as the model presenting the highest (smallest) correlation (root mean squared error, RMSE) values among all examined models. JMA ranked as the second best performing model, followed by ECCC, CPTEC and BoM. The probabilistic assessment for the event "positive precipitation anomaly" revealed that ECMWF presented better discrimination, reliability and resolution when compared to CPTEC and BoM. However, these three models produced overconfident probabilistic predictions. For MJO predictions, CPTEC crosses the 0.5 bivariate correlation threshold at around 19 days when using the mean of 4 ensemble members, presenting similar performance to BoM, JMA and ECCC. Overall, CPTEC proved to be competitive compared to the S2S models investigated, but with respect to ECMWF there is scope to improve the prediction system, likely by a combination of including coupling to an interactive ocean, improving resolution and model parameterization schemes, and better methods for ensemble generation. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of climate simulations produced by the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) of the Center for Weather Forecast and Climate Studies (CPTEC). The model was run over the 1975–2017 period at two spatial resolutions, corresponding to ~ 180 and ~ 100 km, both with 42 vertical levels, following most of the Atmospheric Model Intercomparison Project (AMIP) protocol. In this protocol, observed sea surface temperatures (SSTs) are used as boundary conditions for the atmospheric model. Four ensemble members were run for each of the two resolutions. A series of diagnostics was computed for assessing the model's ability to represent the top of the atmosphere (TOA) radiation, atmospheric temperature, circulation and precipitation climatological features. The representation of precipitation interannual variability, El Niño-Southern Oscillation (ENSO) precipitation teleconnections, the Madden and Julian Oscillation (MJO) and daily precipitation characteristics was also assessed. The model at both resolutions reproduced many observed temperature, atmospheric circulation and precipitation climatological features, despite several identified biases. The model atmosphere was found to be more transparent than the observations, leading to misrepresentation of cloud-radiation interactions. The net cloud radiative forcing, which produces a cooling effect on the global mean climate at the TOA, was well represented by the model. This was found to be due to the compensation between both weaker longwave cloud radiative forcing (LWCRF) and shortwave cloud radiative forcing (SWCRF) in the model compared to the observations. The model capability to represent inter-annual precipitation variability at both resolutions was found to be linked to the adequate representation of ENSO teleconnections. However, the model produced weaker than observed convective activity associated with the MJO. Light daily precipitation over the southeast of South America and other climatologically similar regions was diagnosed to be overestimated, and heavy daily precipitation underestimated by the model. Increasing spatial resolution helped to slightly reduce some of the diagnosed biases. The performed evaluation identified model aspects that need to be improved. These include the representation of polar continental surface and sea ice albedo, stratospheric ozone, low marine clouds, and daily precipitation features, which were found to be larger and last longer than the observed features. [ABSTRACT FROM AUTHOR]

This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E–146.5° E, 14.8° S–27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976–2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December–January–February (DJF) and June–July–August (JJA) over future periods of the early (2011–2040), mid (2041–2070) and late twenty-first century (2071–2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10–20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10–30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections. [ABSTRACT FROM AUTHOR]

The Madden Julian oscillation (MJO) has a systematic influence on the state of the extratropical atmosphere. The physical mechanism involves teleconnections from tropical deep convective heat sources through wave propagation. MJO heating resembles a propagating dipole that cycles through phases on a timescale similar to the response timescale. This complicates the attribution of the response to a well identified phase in the tropical heat source. In this paper the Dynamical Research Empirical Atmospheric Model (DREAM) is used to examine the extratropical response to a moving cyclic MJO-like tropical heat source based on outgoing long-wave radiation data. A set of ensemble forecast experiments and consistent stationary wave solutions are analysed, together with longer equilibrium integrations, to investigate how the extratropical response relates to the phase of the MJO heating. We find that the response is modified by the propagation of the source in a way that depends on the initial phase. It also displays both stationary and transient nonlinearity which can account for an asymmetric response to opposite-phase dipole heat sources. The solution for Indian Ocean heating/West Pacific cooling is more zonally oriented and weaker than the solution for the opposite phase. If the MJO heat source is specified as a continuous cycle, it modifies the time-mean extratropical circulation and generates remote teleconnections. The timing and starting phase of MJO events can greatly modify the relationship between MJO phase and extratropical response, especially when an unforced "resting" period is inserted between simulated MJO cycles. [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]

This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distinguish the regional climatic signal present in atmospheric surface fields from observed data (CPC and TRMM) and 6 RCM simulations belonging to the CORDEX Project. The wavelet used for filtering was Haar wavelet, but a comparative analysis with Daubechies 4 wavelet indicated that meteorological fields or regional indices were not very sensitive to the wavelet selected. Once the longer wavelengths were filtered, we focused on analyzing the spatial variability of extreme rainfall and the spatiotemporal variability of maximum and minimum surface air temperature on a daily basis. The results obtained suggest essential differences in the spatial distribution of the small-scale signal of extreme precipitation between TRMM and regional models, together with a large dispersion between models. While TRMM and CPC register a large signal throughout the continent, the RCMs place it over the Andes Cordillera and some over tropical South America. PAV signal for surface air temperature was found over the Andes Cordillera and the Brazilian Highlands, which are regions characterized by complex topography, and also on the coasts of the continent. The signal came specially from the small-scale stationary component. The transient part is much smaller than the stationary one, except over la Plata Basin where they are of the same order of magnitude. Also, the RCMs and CPC showed a large spread between them in representing this transient variability. The results confirm that RCMs have the potential to add value in the representation of extreme precipitation and the mean surface temperature in South America. However, this condition is not applicable throughout the whole continent but is particularly relevant in those terrestrial regions where the surface forcing is strong, such as the Andes Cordillera or the coasts of the continent. [ABSTRACT FROM AUTHOR]

The paper presents a method for the scale-dependent validation of the spatio-temporal variability in global weather or climate models and for their bias quantification in relation to dynamics. The method provides a relationship between the bias and simulated spatial and temporal variance by a model in comparison with verifying reanalysis data. For the low resolution (T30L8) subset of ERA-20C data, it was found that 80–90% (depending on season) of the global interannual variance is at planetary scales (zonal wavenumbers k = 0−3), and only about 1% of the variance is at scales with k > 7 . The reanalysis is used to validate a T30L8 GCM in two configurations, one with the prescribed sea-surface temperature (SST) and another using a slab ocean model. Although the model with the prescribed SST represents the average properties of surface fields well, the interannual variability is underestimated at all scales. Similar to variability, model bias is strongly scale dependent. Biases found in the experiment with the prescribed SST are largely increased in the experiment using a slab ocean, especially in k = 0 , in scales with missing variability and in seasons with poorly simulated energy distribution. The perfect model scenario (a comparison between the GCM coupled to a slab ocean vs. the same model with prescribed SSTs) shows that the representation of the ocean is not critical for synoptic to subsynoptic variability, but essential for capturing the planetary scales. [ABSTRACT FROM AUTHOR]

Present study attempts to project extreme precipitation indices over 34 different meteorological subdivisions and six homogeneous regions such as Northwest, Central Northeast, Northeast, West Central, Peninsular India and Hilly Region during summer monsoon season in the twenty-first century. For this purpose, the Regional Climate Model version4 (RegCM4) had been run at 50 km horizontal resolution forced with the global model GFDL-ESM2 M, during reference period 1976–2005 for the model validation, and the mid- (2031–2060) and far-future (2070–2099) for projections under RCP8.5 scenario over the South Asia CORDEX domain. In this paper, model simulated precipitation has been validated against IMD, APHRODITE and NCEP/NCAR data sets. The results indicate that RegCM4 captures the important features of seasonal precipitation and various extreme indices over the study area. The RegCM4 has projected an increase in the mean seasonal precipitation by 0.56 mm/day whereas in case of GFDL model the rate is 0.39 mm/day during the far-future relative to the reference period. The heavy precipitation indices are projected to increase more frequently (0.264/decade) than the mean precipitation rate (0.01/decade) over India. The correlations between the extreme precipitation indices and the seasonal mean precipitation are found to be strong. In addition, the consecutive dry days are projected to occur more frequently (3–5 days) over West Central (Telangana, Vidarbha and Marathwada) and west Rajasthan while consecutive wet days are projected to decrease over larger parts of India during far-future. Similarly, 1 day maximum precipitation and the simple daily intensity index are projected to increase consistently from mid- to far- futures over some sub-divisions of West coast, Hilly and Northeast regions. From a spatial probability perspective, model projection indicates more frequent severe drought and flood conditions over India. [ABSTRACT FROM AUTHOR]

This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models' ability to predict extended periods of extreme climate conducive to eight 'billion-dollar' historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecast than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales. [ABSTRACT FROM AUTHOR]

The climate of the Southern Hemisphere (SH) is being affected not only by the global increase in the concentration of greenhouse gases (GHG) but also by the ongoing recovery of stratospheric ozone. The competing roles of both are usually studied by means of state-of-the-art global climate models (GCM) due to the complexity in the atmospheric processes and interactions involved. The main objective of this paper is to determine whether quantitatively similar results can be achieved using a simpler, medium-complexity GCM. This includes addressing the sensitivity of the medium-complexity model to changes in GHG and ozone concentrations as well as looking into the plausible mechanisms driving such patterns of variations in atmospheric circulation, precipitation and temperature extremes. Two sets of simulations are performed: one with fixed sea surface temperatures (SST) and another one making use of a mixed-layer ocean model coupled to the GCM. Results show that the GCM is highly sensitive to changes in the atmospheric composition, and that the largest differences are found when SST are allowed to vary accordingly, particularly in the case of temperature extremes. Specific analyses are also performed on changes in subtropical precipitation over the continental regions of the SH, with moisture budget decomposition being applied in order to provide explanations for the rainfall changes described in the manuscript. [ABSTRACT FROM AUTHOR]

Drifts are always present in models when initialized from observed conditions because of intrinsic model errors; those potentially affect any type of climate predictions based on numerical experiments. Model drifts are usually removed through more or less sophisticated techniques for skill assessment, but they are rarely analysed. In this study, we provide a detailed physical and dynamical description of the drifts in the CNRM-CM5 coupled model using a set of decadal retrospective forecasts produced within CMIP5. The scope of the paper is to give some physical insights and lines of approach to, on one hand, implement more appropriate techniques of initialisation that minimize the drift in forecast mode, and on the other hand, eventually reduce the systematic biases of the models. We first document a novel protocol for ocean initialization adopted by the CNRM-CERFACS group for forecasting purpose in CMIP5. Initial states for starting dates of the predictions are obtained from a preliminary integration of the coupled model where full-field ocean surface temperature and salinity are restored everywhere to observations through flux derivative terms and full-field subsurface fields (below the prognostic ocean mixed layer) are nudged towards NEMOVAR reanalyses. Nudging is applied only outside the 15°S-15°N band allowing for dynamical balance between the depth and tilt of the tropical thermocline and the model intrinsic biased wind. A sensitivity experiment to the latitudinal extension of no-nudging zone (1°S-1°N instead of 15°, hereafter referred to as NOEQ) has been carried out. In this paper, we concentrate our analyses on two specific regions: the tropical Pacific and the North Atlantic basins. In the Pacific, we show that the first year of the forecasts is characterized by a quasi-systematic excitation of El Niño-Southern Oscillation (ENSO) warm events whatever the starting dates. This, through ocean-to-atmosphere heat transfer materialized by diabatic heating, can be viewed for the coupled model as an efficient way to rapidly adjust to its own biased climate mean state. Weak cold ENSO events tend to occur the second year of the forecast due to the so-called discharge-recharge mechanism while the spurious oscillatory behavior is progressively damped. The latter mechanism is much more pronounced in retrospective forecasts initialized from the NOEQ configuration for which the ENSO flip-flop is still detectable at leadtime 4 year. Associated atmospheric teleconnections interfere worldwide with regional drifts, especially in the North Pacific and more remotely in the North Atlantic. In the latter basin, the drift can be interpreted as the model response to intrinsic atmospheric circulation biases found in the stand-alone atmosphere component of the model, which project onto the negative phase of the North Atlantic Oscillation. A fast adjustment (up to ~5-year leadtime) occurs leading to a rapid slackening of both the vertical (Atlantic meridional overturning circulation) and horizontal circulations, especially in the subpolar gyre. Slower adjustment of the entire water masses distribution in the North Atlantic then takes over involving several mechanisms. We show that a weak feedback is locally present between the atmospheric circulation and the ocean drift that controls the timescale of the setting of the coupled model biases. [ABSTRACT FROM AUTHOR]

A significant fraction of multidecadal fluctuations in the reanalysis-based gridded estimates of the observed climate variability over the past century and a half lie outside of the envelope generated by ensembles of climate-model historical simulations. Several pattern-recognition methods have been previously used to map out a truly global reach of the observed vs. simulated climate-data differences; in our own work we dubbed these global discrepancies the stadium wave to highlight their most striking spatiotemporal characteristic. Here we used a novel combination of such methods in conjunction with a large multi-model ensemble and two popular twentieth-century reanalysis products to: (i) succinctly describe the geographical evolution of the observed stadium wave in the annually sampled near-surface atmospheric temperature and mean sea-level pressure fields in terms of three basic patterns; (ii) show the robustness of this identification with respect to methodological details, including the demonstration of the truly global character of the stadium wave; and (iii) provide essential clues to its dynamical origin. Part I of this study decomposed all input time series into the forced signal and the residual internal variability; multi-model forced-signal estimates were also decomposed into their common-evolution part and the individual-model residuals. Analysis of the latter residuals suggests a contribution to the stadium-wave dynamics from a delayed climate response to variable external forcing despite the observed stadium-wave patterns’ exhibiting the magnitudes and the level of global teleconnectivity unmatched by the forced-signal residuals. Part III of this paper will compare the observed stadium wave with the model simulated internal patterns. [ABSTRACT FROM AUTHOR]

Climate models are useful tools for monthly to decadal prediction of the evolution of climate. This study assesses how CMIP6 models represent soil moisture-latent heat regimes and coupling processes between the land and atmosphere. Metrics considered are terrestrial and atmospheric coupling indices to show the nature and strength of the coupling over Africa, focusing on the March to May (MAM) and June to August (JJA) seasons over East, Central, and West Africa. Characterization of the annual cycle indicates that model biases are highest during the peak of the rainfall season and least during the dry season, while soil moisture biases correspond with rainfall. Models show appreciable sensitivity to regional characteristics; there was model consensus in representing East Africa and the Sahel as regions of limited soil moisture, while major differences were noted in the wet regime over Central Africa. Most CMIP6 models tend to overestimate the strength of the terrestrial and atmospheric coupling pathways over East and Southern Africa. Inter-model differences in coupling indices could be traced to their inter-annual variability rather than the mean biases of the variables considered. These results encourage further advancement of land surface schemes in the next generation of climate models for a better representation of climate over Africa. [ABSTRACT FROM AUTHOR]

The Turkana low-level jet stream (TJ) is important to climatic conditions over northern Kenya and East Africa. The representation of the TJ in climate models varies due to the TJ interaction with Turkana channel that is influenced by model resolution and influences the model representation of the regional climate. This study compares features of the TJ in CMIP6 AMIP model simulations with ERA5. Models reveal climatological wind speeds that match those of the reanalysis from the ERA5 at the jet entrance (13 m/s) but lower magnitudes of wind speed and vertical shears compared to ERA5 within the Turkana channel. The models with slowest wind speeds, have a flattened Turkana channel and fail to exhibit the terrain constriction at 37° E which otherwise aids in accelerating winds to form a jet core. Furthermore, they fail to represent the narrowing of the channel as in ERA5, thereby forming blocking walls in the channel, forcing vertical ascent and mixing, and weakening shear. This boosting of ascent motion promotes rainfall formation and enhances wet anomalies at the exit of the TJ when the jet stream is weaker. By applying a new narrowing index, we demonstrate the need to improve topography details in the CMIP6 models, particularly those with resolution coarser than 1.5°, in order to properly simulate the TJ and the observed rainfall over the northwestern areas of eastern Africa. [ABSTRACT FROM AUTHOR]

We evaluate and compare the simulation of the main features (low-level westerlies (LLWs) and the Congo basin (CB) cell) of low-level circulation in Central Equatorial Africa (CEA) with eight climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and the corresponding eight previous models from CMIP5. Results reveal that, although the main characteristics of the two features are reasonably well depicted by the models, they bear some biases. The strength of LLWs is generally overestimated in CMIP5 models. The overestimation is attributed to both divergent and rotational components of the total wind with the rotational component contributing the most in the overestimation. In CMIP6 models, thanks to a better performance in the simulation of both divergent and rotational circulation, LLWs are slightly less strong compared to the CMIP5 models. The improvement in the simulated divergent component is associated with a better representation of the near-surface pressure and/or temperature difference between the Central Africa landmass and the coastal Atlantic Ocean. Regarding the rotational circulation, and especially for HadGEM3-GC31-LL and BCC-CSM2-MR, a simulated higher 850 hPa pressure is associated with less pronounced negative vorticity and a better representation of the rotational circulation. Most CMIP5 models also overestimate the CB cell intensity and width in association with the simulated strength of LLWs. However, in CMIP6 models, the strength of key cell characteristics (intensity and width) are reduced compared to CMIP5 models. This depicts an improvement in the representation of the cell in CMIP6 models and this is associated with the improvement in the simulated LLWs. [ABSTRACT FROM AUTHOR]

How climate change will unfold in the years to come is a central topic in today's environmental debate, in particular at the regional level. While projections using large ensembles of global climate models consistently indicate a future decrease in summer precipitation over southern Europe and an increase over northern Europe, individual models substantially modulate these distinct signals of change in precipitation. So far model improvements and higher resolution from regional downscaling have not been seen as able to resolve these disagreements. In this paper we assess whether 2 decades of investments in large ensembles of downscaling experiments with regional climate model simulations for Europe have contributed to a more robust model assessment of the future climate at a range of geographical scales. We study climate change projections of European seasonal temperature and precipitation using an ensemble-suite comprised by all readily available pan-European regional model projections for the twenty-first-century, representing increasing model resolution from ~ 50 to ~ 12 km grid distance, as well as lateral boundary and sea surface temperature conditions from a variety of global model simulations. Employing a simple scaling with global mean temperature change we identify emerging robust signals of future seasonal temperature and precipitation changes also found to resemble current observed trends, where these are judged to be statistically significant. [ABSTRACT FROM AUTHOR]

The potential impacts of floods are of significant concern to our modern society raising the need to identify and quantify all the uncertainties that can impact their simulations. Climate simulations at finer spatial resolutions are expected to bring more confidence in these hydrological simulations. However, the impact of the increasing spatial resolutions of climate simulations on floods simulations has to be evaluated. To address this issue, this paper assesses the sensitivity of summer–fall flood simulations to the Canadian Regional Climate Model (CRCM) grid resolution. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim reanalysis at 0.44°, 0.22° and 0.11° resolutions are analysed at a daily time step for the 1981–2010 period. Raw CRCM5 precipitation and temperature outputs are used as inputs in the simple lumped conceptual hydrological model MOHYSE to simulate streamflows over 50 Quebec (Canada) basins. Summer–fall flooding is analysed by estimating four flood indicators: the 2-year, 5-year, 10-year and 20-year return periods from the CRCM5-driven streamflows. The results show systematic impacts of spatial resolution on CRCM5 outputs and seasonal flood simulations. Floods simulated with coarser climate datasets present smaller peak discharges than those simulated with the finer climate outputs. Smaller catchments show larger sensitivity to spatial resolution as more detail can be obtained from the finer grids. Overall, this work contributes to understanding the sensitivity of streamflow modelling to the climate model's resolution, highlighting yet another uncertainty source to consider in hydrological climate change impact studies. [ABSTRACT FROM AUTHOR]

In this paper, mechanisms for the formation of quasi-stationary extratropical wave trains associated with four distinct winter patterns of seasonal mean tropical Pacific convection anomalies, including the western CP (W-CP) pattern, La Niña (LN) pattern, CP El Niño (CPEN) pattern and EP El Niño (EPEN) pattern, are investigated by utilizing observational and six Atmospheric Model Intercomparison Project Phase 5 (AMIP5) high-skilled models (ACCESS1.0, CanAM4, CCSM4, CMCC-CM, HadGEM2-A, MPI-ESM-LR) datasets. The observational results show that tropical Pacific forcing induces upper-level anomalous Rossby wave source that force vorticity over the central North Pacific through modulating the local Hadley circulation, then excite eastward propagation of quasi-stationary planetary waves penetrating into the North Atlantic, and finally establish the associated teleconnections in mid- and high-latitudes by local synoptic eddy–mean flow interaction. Different structures of wave trains may be explained by evident differences in intensity and scale of the extratropical vorticity forcing originating from distinct convection anomalies over the tropical Pacific. The MME results further confirm the possible physical processes obtained from observations, but there still exist some significant differences. These cover the North Pacific and North America and can possibly be attributed to the models' simulation ability of extratropical vorticity forcing and local storm track intensity. In addition, the stratosphere–troposphere interaction plays an important role for extratropical atmospheric circulation anomalies. The stratospheric polar vortex not only modulates the underlying wave train, especially for the North Atlantic/Europe sector, but is also affected by the upper-tropospheric disturbance in high-latitudes through the upward wave activity flux. [ABSTRACT FROM AUTHOR]

Understanding the processes responsible for precipitation and its future change is important to develop plausible and sustainable climate change adaptation strategies, especially in regions with few available observed data like Congo Basin (CB). This paper investigates the atmospheric circulation processes associated with climate model biases in CB rainfall, and explores drivers of projected rainfall changes. Here we use an ensemble of simulations from the Swedish Regional Climate Model (RCM) RCA4, driven by eight General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), for the 1.5 ∘ C and 2 ∘ C global warming levels (GWLs), and under the representative concentration pathways (RCPs) 4.5 and 8.5. RCA4 captures reasonably well the observed patterns of CB rainfall seasonality, but shows dry biases independent of seasons and large scale driving atmospheric conditions. While simulations mimic observed peaks in transition seasons (March–May and September–November), the rain-belt is misplaced southward (northward) in December–February (June–August), reducing the latitudinal extent of rainfall. Moreover, ERA-Interim reanalysis driven RCM simulation and RCM–GCM combinations show similar results, indicating the dominance of systematic biases. Modelled dry biases are associated with dry upper-tropospheric layers, resulting from a western outflow stronger than the eastern inflow and related to the northern component of African Easterly Jet. From the analysis of the climate change signal, we found that regional scale responses to anthropogenic forcings vary across GWLs and seasons. Changes of rainfall and moisture divergence are correlated, with values higher in March–May than in September–November, and larger for global warming of 2.0 ∘ C than at 1.5 ∘ C . There is an increase of zonal moisture divergence fluxes in upper atmospheric layers ( > 700 hPa ) under RCP8.5 compared to RCP4.5. Moreover, it is found that additional warming of 0.5 ∘ C will change the hydrological cycle and water availability in the CB, with potential to cause challenges to water resource management, agriculture, hydro-power generation, sanitation and ecosystems. [ABSTRACT FROM AUTHOR]

The usefulness of a set of climate change projections largely depends on how well it spans a range of outcomes consistent with known uncertainties. Here, we present exploratory work towards developing a strategy to select variants of a state-of-the-art but expensive climate model suitable for climate projection studies. The strategy combines information from a set of relatively cheap, idealized perturbed parameter ensemble (PPE) and CMIP5 multi-model ensemble (MME) experiments, and uses two criteria as the basis to select model variants for a PPE suitable for future projections: (a) acceptable model performance at two different timescales, and (b) maintaining diversity in model response to climate change. This second part of a pair of papers builds upon Part I in which we established a strong relationship between model errors at weather and climate timescales across a PPE for a variety of key variables. This relationship is used to filter out parts of parameter space that do not give credible simulations of present day climate, while minimizing the impact on ranges in forcings and feedbacks that drive model responses to climate change. We use statistical emulation to explore the parameter space thoroughly, and demonstrate that about 90% can be filtered out without affecting diversity in global-scale climate change responses. This leads to the identification of plausible parts of parameter space from which model variants can be selected for projection studies. We selected and ran 50 variants from the plausible parameter combinations and validated the emulator predictions. Comparisons with the CMIP5 MME demonstrate that our approach can produce a set of plausible model variants that span a relatively wide range in model response to climate change. We also highlight how the prior expert-specified ranges for uncertain model parameters are constrained as a result of our methodology, and discuss recommendations for future work. [ABSTRACT FROM AUTHOR]

A sequence of ensemble forecasts permits the estimation of the "predictable" and "unpredictable" components of the forecasts and hence the predictability of the system. Predictability is a feature of a physical and/or mathematical system which characterizes its "ability to be predicted" as measured, for instance, by the rate at which initially close states separate. Modern climate models may be used to predict climate evolution on seasonal to decadal timescales and they may also be used to estimate predictability. The predictability in this case measures the model's ability to predict its own evolution and, for a well behaved climate model, this may be a useful measure of the predictability of the climate system itself. Forecast skill, where forecasts are compared to observations, indicates a forecast system's "ability to predict" the evolution of the climate system. Common measures of skill include correlation, mean square error and mean square skill score. When the model is used to predict its own evolution, rather than the evolution of the actual climate system, the same measures of skill may be used but are then predictability measures and termed measures of "potential skill". The expectation is that the model's skill in predicting its own evolution will be greater than its ability to predict the actual evolution of the system in the face of observational uncertainty and model deficiencies. The further expectation is that the differences between potential and actual skill can give an indication of where and to what extent there is the potential for improvement in forecast skill. Predictability studies may be undertaken with available models but it may not be immediately obvious which statistical conditions are necessary if potential skill is to be a reasonable estimate of attainable skill. The purpose of this paper is to investigate and illustrate the spatial and temporal behaviour of the predictable and unpredictable components of surface air temperature in a decadal prediction experiment and to discuss some of the implications for predictability studies and for analyzing model behaviour. Formal relationships between potential, actual, and attainable skill are developed which, however, provide only modest constraints on model results if they are to be used to infer predictability. The results of various scalings of the forecasts on potential and actual skill and their relationship are also considered. Differences in the actual and potential forecast skill between models and/or model versions can be partitioned into components associated with known statistics as a guide to forecast improvement. [ABSTRACT FROM AUTHOR]

The enhancement of warming rates with elevation, so-called elevation-dependent warming (EDW), is one of the regional, still not completely understood, expressions of global warming. Sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming trends in the recent decades, leading to serious impacts on mountain ecosystems and downstream. In this paper we use a state-of-the-art Global Climate Model (EC-Earth) to investigate the impact of model spatial resolution on the representation of this phenomenon and to highlight possible differences in EDW and its causes in different mountain regions of the Northern Hemisphere. To this end we use EC-Earth climate simulations at five different spatial resolutions, from ∼ 125 to ∼ 16 km, to explore the existence and the driving mechanisms of EDW in the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau-Himalayas. Our results show that the more frequent EDW drivers in all regions and seasons are the changes in albedo and in downward thermal radiation and this is reflected in both daytime and nighttime warming. In the Tibetan Plateau-Himalayas and in the Greater Alpine Region, an additional driver is the change in specific humidity. We also find that, while generally the model shows no clear resolution dependence in its ability to simulate the existence of EDW in the different regions, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions. Moreover, we find that the role of internal climate variability can be significant in modulating the EDW signal, as suggested by the spread found in the multi-member ensemble of the EC-Earth experiments which we use. [ABSTRACT FROM AUTHOR]

Using the Weather Research and Forecasting (WRF) model, this paper analyzes the spatiotemporal features of heat waves in 20-year regional climate simulations over East Asia, and investigates the capability of WRF to reproduce observational heat waves in China. Within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX), the WRF model is driven by the ERA-Interim (ERAIN) reanalysis, and five continuous simulations are conducted from 1989 to 2008. Of these, four runs apply the interior spectral nudging (SN) technique with different wavenumbers, nudging variables and nudging coefficients. Model validations show that WRF can reasonably reproduce the spatiotemporal features of heat waves in China. Compared with the experiment without SN, the application of SN is effectie on improving the skill of the model in simulating both the spatial distributions and temporal variations of heat waves of different intensities. The WRF model shows advantages in reproducing the synoptic circulations with SN and therefore yields better representations for heat wave events. Besides, the SN method is able to preserve the variability of large-scale circulations quite well, which in turn adjusts the extreme temperature variability towards the observation. Among the four SN experiments, those with stronger nudging coefficients perform better in modulating both the spatial and temporal features of heat waves. In contrast, smaller nudging coefficients weaken the effects of SN on improving WRF's performances. [ABSTRACT FROM AUTHOR]

Variational approaches to data assimilation, and weakly constrained four dimensional variation (WC-4DVar) in particular, are important in the geosciences but also in other communities (often under different names). The cost functions and the resulting optimal trajectories may have a probabilistic interpretation, for instance by linking data assimilation with maximum aposteriori (MAP) estimation. This is possible in particular if the unknown trajectory is modelled as the solution of a stochastic differential equation (SDE), as is increasingly the case in weather forecasting and climate modelling. In this situation, the MAP estimator (or "most probable path" of the SDE) is obtained by minimising the Onsager-Machlup functional. Although this fact is well known, there seems to be some confusion in the literature, with the energy (or "least squares") functional sometimes been claimed to yield the most probable path. The first aim of this paper is to address this confusion and show that the energy functional does not, in general, provide the most probable path. The second aim is to discuss the implications in practice. Although the mentioned results pertain to stochastic models in continuous time, they do have consequences in practice where SDE's are approximated by discrete time schemes. It turns out that using an approximation to the SDE and calculating its most probable path does not necessarily yield a good approximation to the most probable path of the SDE proper. This suggest that even in discrete time, a version of the Onsager-Machlup functional should be used, rather than the energy functional, at least if the solution is to be interpreted as a MAP estimator. [ABSTRACT FROM AUTHOR]

The Andes Cordillera plays a role in driving moisture and heat from tropical onto subtropical South America. It forces the development of a lee-side trough that covers most of western Argentina and a low-level jet that maximizes over Paraguay, eastern Bolivia and northern Argentina and is tightly linked to precipitation variability over much of central and southeastern South America. Its steep slopes and the large zonal gradients in topography between the Equator and 40°S are misrepresented in climate simulations using Global Climate Models (GCM) with resolutions coarser than about 100 km, since they naturally have a poor representation of the Andes and related circulation features. This paper analyses the impact of varying artificially the altitude of the Andes Cordillera in a GCM as well as increasing the horizontal resolution to study how these variations determine moisture fluxes and precipitation over selected regions of South America. Results show that the height of the Andes is crucial in shaping moisture fluxes pathways onto subtropical South America all year long. In particular, the low-level jet is only simulated when the Andes heights are doubled. At the same time, the relationship between the Andes shape and the location of the Bolivian High in summer is also discussed. In terms of precipitation, the lowest bias in the simulations is achieved when the horizontal resolution is increased, while in particular near the Andes foothills the simulated annual rainfall is largely determined by the Mountains shape. [ABSTRACT FROM AUTHOR]

The effect of increasing atmospheric resolution on prediction skill of El Niño southern oscillation phenomenon in climate forecast system model is explored in this paper. Improvement in prediction skill for sea surface temperature (SST) and winds at all leads compared to low resolution model in the tropical Indo-Pacific basin is observed. High resolution model is able to capture extreme events reasonably well. As a result, the signal to noise ratio is improved in the high resolution model. However, spring predictability barrier (SPB) for summer months in Nino 3 and Nino 3.4 region is stronger in high resolution model, in spite of improvement in overall prediction skill and dynamics everywhere else. Anomaly correlation coefficient of SST in high resolution model with observations in Nino 3.4 region targeting boreal summer months when predicted at lead times of 3-8 months in advance decreased compared its lower resolution counterpart. It is noted that higher variance of winds predicted in spring season over central equatorial Pacific compared to observed variance of winds results in stronger than normal response on subsurface ocean, hence increases SPB for boreal summer months in high resolution model. [ABSTRACT FROM AUTHOR]