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The global ocean overturning circulation, critically dependent on the global density stratification, plays a central role in regulating climate evolution. While it is well known that the global stratification profile exhibits a strong dependence to Southern Ocean dynamics and in particular to wind and buoyancy forcing, we demonstrate here that the stratification is also acutely sensitive to the mesoscale eddy energy dissipation timescale. Within the context of a global ocean circulation model with an energy constrained mesoscale eddy parameterization, it is shown that modest variations in the eddy energy dissipation timescale lead to significant variations in key metrics relating to ocean circulation, namely the Antarctic Circumpolar Current transport, Atlantic Meridional Overturning Circulation strength, and global ocean heat content, over long timescales. The results highlight a need to constrain uncertainties associated with eddy energy dissipation for climate model projections over centennial timescales and also for paleoclimate simulations over millennial timescales. Plain Language Summary: The ocean is populated by "eddies", analogous to weather systems in the atmosphere, but occurring on much smaller scales of typically 10–100 km. Recent advances in our understanding of the circulation of the Southern Ocean, which connects all of the major ocean basins to the north, have revealed a crucial role for the energy balance of Southern Ocean eddies. In this paper, we show that the timescale over which energy is removed from Southern Ocean eddies has a dramatic impact on the following: (a) the strength of the Antarctic Circumpolar Current, the largest current in the global ocean; (b) the strength of the Atlantic meridional overturning circulation, responsible in part for the relatively mild climatic conditions over northwestern Europe; and (c) global ocean heat content, a key parameter in the global climate system. These results have significant implications for both past and future climates, and highlight the importance of combining observational, theoretical and modeling efforts to better understand and constrain the energy balance of ocean eddies. Key Points: Key metrics of global ocean circulation (Antarctic Circumpolar Current transport, Atlantic Meridional Overturning Circulation strength, and Ocean Heat Content anomaly) acutely sensitive to an eddy energy dissipation timescaleModest variations in the dissipation timescale has a comparable effect to significant variations in the Southern Ocean wind forcingConstraints on the dissipation timescale critical to longtime integrations of ocean climate models such as paleoclimate scenarios [ABSTRACT FROM AUTHOR]

In this paper, we show that the projected global temperature change from CMIP5 models is remarkably similar to that obtained using simple regression models relating the global temperature to the atmospheric concentrations of CO2. This result is strengthened when we consider the projections obtained using the CMIP6 models. [ABSTRACT FROM AUTHOR]

The Eastern Mediterranean surface circulation is highly energetic, composed of structures interacting stochastically. However, some main features are still debated, and the behavior of some fine-scale dynamics and their role in shaping the general circulation is yet unknown. In the following paper, we use an unsupervised neural network clustering method to analyze the long-term variability of the different mesoscale structures. We decompose 26 years of altimetric data into clusters reflecting different circulation patterns of weak and strong flows with either strain or vortex-dominated velocities. The vortex-dominated cluster is more persistent in the western part of the basin, which is more active than the eastern part due to the strong flow along the coast, interacting with the extended bathymetry and engendering continuous instabilities. The cluster that reflects a weak flow dominated the middle of the basin, including the Mid-Mediterranean Jet (MMJ) pathway. However, the temporal analysis shows a frequent and intermittent occurrence of a strong flow in the middle of the basin, which could explain the previous contradictory assessment of MMJ existence using in-situ observations. Moreover, we prove that the Levantine Sea is becoming more and more energetic as the activity of the main mesoscale features is showing a positive trend. [ABSTRACT FROM AUTHOR]

Climate models project an intensification of the wintertime North Atlantic Ocean storm track, over its downstream region, by the end of this century. Previous studies have suggested that ocean–atmosphere coupling plays a key role in this intensification, but the precise role of the different components of the coupling has not been explored and quantified. In this paper, using a hierarchy of ocean coupling experiments, we isolate and quantify the respective roles of thermodynamic (changes in surface heat fluxes) and dynamic (changes in ocean heat flux convergence) ocean coupling in the projected intensification of North Atlantic transient eddy kinetic energy (TEKE). We show that dynamic coupling accounts for nearly all of the future TEKE strengthening as it overcomes the much smaller effect of surface heat flux changes to weaken the TEKE. We further show that by reducing the Arctic amplification in the North Atlantic, ocean heat flux convergence increases the meridional temperature gradient aloft, causing a larger eddy growth rate and resulting in the strengthening of North Atlantic TEKE. Our results stress the importance of better monitoring and investigating the changes in ocean heat transport, for improving climate change adaptation strategies. Significance Statement: By the end of this century, the North Atlantic Ocean storm track is projected to intensify on its eastward flank. Such intensification will have large societal impacts, mostly over western Europe. Thus, it is critical to better understand the mechanism underlying the intensification of the storm track. Here we investigate the role of ocean coupling in the future intensification of the North Atlantic storm track and find that ocean heat transport processes are responsible for the strengthening of the storm track. Our results suggest that better monitoring the changes in ocean heat transport will hopefully improve climate change adaption strategies. [ABSTRACT FROM AUTHOR]

Assessing the effects of climate change is a key component of the sustainable management of water resources and food security. In this paper, general circulation models (GCM) were evaluated using historical information for Birjand synoptic station, Iran. Modeling was performed using 35 models of the Fifth Climate Change Report for 27 historical periods. The results showed that longer annual periods are the most suitable periods for hydrological simulation when data are available. Therefore, the periods of 1960-1990 may be the most appropriate periods due to the adaptation to the observation data. To estimate rainfall, periods with more years showed a more accurate forecast of the future. Moreover, the results showed more changes in the RCP 8.5 scenario than in the RCP 4.5 scenario. According to the comparison of models, the NorESM1-M model with a root mean square error (RMSE) of 0.091 and the GISS-E2-R model with a low percent bias (PBIAS) can be an appropriate model for estimating rainfall. [ABSTRACT FROM AUTHOR]

Titan’s stratosphere has been observed in a superrotation state, where the atmosphere rotates many times faster than the surface does. Another characteristic of Titan’s atmosphere is the presence of a thick haze layer. In this paper, we performed numerical experiments using a general circulation model to explore the effects of the haze layer on the stratospheric superrotation. We employed a semigray radiation model of Titan’s atmosphere following McKay et al., which takes account of sunlight absorption by the haze particles. The phase change of methane or seasonal changes were not taken into account. Our model with radiation parameters tuned for Titan yielded a global eastward wind around the equator with larger velocities at higher altitudes, except at around 70 km, after 105 Earth days. Although the atmosphere is not in an equilibrium state, the zonal wind profiles are approximately consistent with the observed one. By changing the parameters of the radiation model, we found that the intensity and the location of the maximum zonal wind velocity highly depended on the optical thickness and the altitude of the haze layer, respectively. Analysis of our experiments suggests that the quasi-stationary stratospheric superrotation is maintained by the balance between the meridional circulation decoupled from the surface and the eddies that transport angular momentum equatorward. This is different from, but similar to, the so-called Gierasch mechanism, in which momentum is supplied from the surface. This structure may explain the no-wind region at about 80 km in altitude. [ABSTRACT FROM AUTHOR]

The Earth's magnetosheath and cusps emit soft X‐rays due to the charge exchange between highly charged solar wind ions and exospheric hydrogen atoms. The Lunar Environment Heliospheric X‐ray Imager and Solar wind Magnetosphere Ionosphere Link Explorer missions are scheduled to image the Earth's dayside magnetosphere system in soft X‐rays to investigate global‐scale magnetopause reconnection modes under varying solar wind conditions. The exospheric neutral hydrogen density distribution, especially the value of this density at the subsolar magnetopause is of particular interest for understanding X‐ray emissions near this boundary. This paper estimates the exospheric density during solar minimum using the X‐ray Multimirror Mission (XMM) astrophysics observatory. We selected an event on 12 November 2008 from the XMM data archive, which detects soft X‐rays of magnetosheath origin while solar wind and interplanetary magnetic field conditions are relatively constant. During the event the location of the magnetopause was measured in situ by the THEMIS mission, thus the location of the solar wind ions responsible for the magnetosheath emission is well constrained by observation. We estimated the exospheric density using the Open Geospace Global Circulation Model (OpenGGCM) and a spherically symmetric exosphere model. The ratio of the magnetosheath plasma flux between the OpenGGCM model and the THEMIS, was nearly 1, which means the magnetohydrodynamic model reasonably reproduces the magnetosheath plasma conditions. The OpenGGCM magnetosheath parameters were used to deconvolve soft X‐rays of exospheric origin from the XMM signal. The lower‐limit of the exospheric density of this solar minimum event is 36.8 ± 11.7 cm−3 at 10 RE subsolar location. Key Points: From 11 years of X‐ray Multimirror Mission‐Newton X‐ray observations, we identified 193 intervals that can be used to estimate the exospheric neutral densityWe derived a solar minimum exospheric neutral density from the 12 November 2008 event using a global MagnetoHydroDynamics model and a simplified exosphere modelThe density estimate is 36.8 cm−3 near the subsolar magnetopause and is likely a lower‐limit value [ABSTRACT FROM AUTHOR]

Climate change is expected to exacerbate the extremes in the climate variables. Being prone to harsh climate impacts, it is very crucial to study extreme rainfall-induced flooding for short durations over regions that are rapidly growing. One way to study the extremes is by the application of the Intensity-Duration-Frequency (IDF) curves. The annual maximum rainfall intensity (AMRI) characteristics are often used to construct these IDF curves that are being used in several infrastructure designs for urban areas. Thus, there is a need to obtain high temporal and spatial resolution rainfall information. Many urban areas of developing countries lack long records of short-duration rainfall. The shortest duration obtained is normally at a daily scale/24 h. Thus, it is very crucial to find a methodology to construct IDF curves for short-duration rainfall (sub-daily) for these urban areas. Vietnam is a developing country with rapidly increasing population as well as urbanization. The fast extension of urban area that does not have adequate preparedness to cope with climate change is certainly a big risk to life and economy. The limitation in studying impacts over many regions of Vietnam is the need for robust and sufficient data, both spatial and temporal. To overcome this limitation, this paper describes constructing IDF curves using 6 hourly rainfall AMRI output from a regional climate model (RCM) that downscaled a global climate model (GCM) output at high spatial and temporal resolutions. The study region is Hanoi, the capital city of Vietnam. The sub-daily IDF curves for current and future climate for Hanoi were constructed from 1 to 24 h based on the simple scaling approach. The findings indicate that it is likely that Hanoi might experience more flooding conditions in the future with the AMRI increasing between 34 and 48% for all return periods from 10 to 200 years. The methodology adopted in this paper is suitable for similar ungauged areas elsewhere and will provide useful information in devising adequate planning strategies for drainage designs. [ABSTRACT FROM AUTHOR]

A number of studies have suggested that heat waves will increase in frequency, intensity, and duration under anthropogenic global warming. However, changes to heat extremes and temperature distributions in general are less understood in regions of complex terrain and substantial land-water contrasts. Such surface variations are important in the northwest United States, where synoptic- and mesoscale circulations interact with local topography and land-water interfaces to produce heat waves that have substantial impacts on fire weather, air quality, wind energy, and the population in general. Thus it is crucial to identify how the synoptic- and mesoscale circulations that drive such local extremes will change in a warming world. This paper analyzes changes to the conditions associated with heat extremes over the northwest United States by utilizing global and regional climate models. A maximum in 700-hPa warming is projected over the northwest United States and southwest Canada, with low-level warming attenuated near the coast. Soil moisture declines are projected over the region, which further enhances future extremes. It is found that low-level zonal wind distributions over the northwest United States become narrower, leading to fewer days with strong offshore flow. This is important since extreme warming events are associated with the strongest offshore/downslope flow. Historical and future regional temperature distributions are described and it is shown that although CMIP5 models predict increases in heat extremes for west Oregon and Washington, these increases are less than for inland areas. [ABSTRACT FROM AUTHOR]

In this paper, we report on the mixing structures and transport properties of the Adriatic Sea surface, as a semi-enclosed basin of the Mediterranean Sea, from October 2006 until December 2011. Lagrangian transport models were used to simulate synthetic trajectories from the mean flow fields obtained by the Massachusetts Institute of Technology general circulation model implemented for the Adriatic. We examine the dispersion properties of numerical pair particles, through the calculation of time-averaged finite-size Lyapunov exponents (FSLEs) in the Adriatic Sea during selected months in each year. The results show the significant effects of river runoff and wind forcing, especially the Bora wind field, on the mixing activities of numerical pair particles by the generation of vortices, which appear as a tangle of filaments on the FSLE maps. The stretch/compression lines, which contain high values of the FSLEs, work as robust transport barriers, most having been detected along boundary currents on the eastern and western flanks of the Adriatic, particularly during winter. Numerical experiments have indicated that stable flows, with less mixing activity, occur in the northern part of the Adriatic in June and September of each year, while the Southern Adriatic Pit has flows with larger seasonal fluctuations and high values of eddy kinetic energy because of the influence of wind and energetic currents entering from the Ionian Sea. [ABSTRACT FROM AUTHOR]

Data‐driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational‐Multiscale (VMS) Artificial Neural Network (ANN) discretization which makes no a priori assumptions on the model form, and is only restricted in its accuracy by the precision of the ANN. Using a simplified problem, we demonstrate that good predictions of the required closure terms can be obtained with relatively compact ANN architectures. We then turn our attention to the stability of the VMS‐ANN discretization in the context of a single implicit time step. It is demonstrated that the ANN parameterization introduces nonphysical solutions to the governing equations that can significantly affect or prevent convergence. We show that enriching the training data with nonphysical states from intra‐time step iterations is an effective remedy. This indicates that the lack of representative ANN‐induced errors in our original, exact training inputs underpin the observed instabilities. In turn, this suggests that data set enrichment might aid in resolving instabilities that develop over several time steps. Plain Language Summary: Computer models of weather and climate have coarse resolutions, which prevent them from accurately predicting the effect of small atmospheric motions, for instance in low‐lying clouds, on the global climate. Recent studies indicate that improvements might be had by using models for the small motions that are informed purely by data, for example Artificial Neural Network (ANN) models. However, capitalizing on this potential is challenging in practice, since ANNs can introduce instabilities to the numerical model for larger‐scale motions. In this study, we zero in on these instabilities by introducing a model framework in which prediction accuracy is only limited by an ANN's ability to predict the net influence of small atmospheric motions. We contextualize this framework by contrasting it to more commonly used approaches. Using a simple test case based on the motions underpinning the development of low clouds, we illustrate that standard training procedures do not prepare ANN models for their interaction with the rest of the numerical model, and that this can contribute to numerical instability. We demonstrate an effective remedy for a specific type of instability, and suggest how this technique might also be used to treat other types of instabilities prompted by ANN models. Key Points: Artificial Neural Networks (ANNs) can parameterize exact closure terms of a Variational Multiscale method without a priori model form assumptionsThe ANN parameterization outperforms state‐of‐the‐art schemes in offline testsInstabilities in the numerical model comprising the ANN parameterization are amendable through data set enrichment [ABSTRACT FROM AUTHOR]

In the southern winter polar stratosphere, the distribution of gravity wave momentum flux in many state-of-the-art climate simulations is inconsistent with long-time satellite and superpressure balloon observations around 60 ∘ S. Recent studies hint that a lateral shift between prominent gravity wave sources in the tropospheric mid-latitudes and the location where gravity wave activity is present in the stratosphere causes at least part of the discrepancy. This lateral shift cannot be represented by the column-based gravity wave drag parameterisations used in most general circulation models. However, recent high-resolution analysis and re-analysis products of the European Centre for Medium-Range Weather Forecasts Integrated Forecast System (ECMWF-IFS) show good agreement with the observations and allow for a detailed investigation of resolved gravity waves, their sources, and propagation paths. In this paper, we identify resolved gravity waves in the ECMWF-IFS analyses for a case of high gravity wave activity in the lower stratosphere using small-volume sinusoidal fits to characterise these gravity waves. The 3D wave vector together with perturbation amplitudes, wave frequency, and a fully described background atmosphere are then used to initialise the Gravity Wave Regional or Global Ray Tracer (GROGRAT) gravity wave ray tracer and follow the gravity waves backwards from the stratosphere. Finally, we check for the indication of source processes on the path of each ray and, thus, quantitatively attribute gravity waves to sources that are represented within the model. We find that stratospheric gravity waves are indeed subject to far (>1000 km) lateral displacement from their sources, which already take place at low altitudes (<20 km). Various source processes can be linked to waves within stratospheric gravity wave (GW) patterns, such as the orography equatorward of 50 ∘ S and non-orographic sources above the Southern Ocean. These findings may explain why superpressure balloons observe enhanced gravity wave momentum fluxes in the lower stratosphere over the Southern Ocean despite an apparent lack of sources at this latitude. Our results also support the need to improve gravity wave parameterisations to account for meridional propagation. [ABSTRACT FROM AUTHOR]

There has been a growing interest in model-wise uncertainty decomposition, which quantifies contribution of individual models such as emission scenarios, global circulation models, bias correction techniques and hydrological models, to the total uncertainty of a hydrological projection. However, little research has been conducted for model-wise uncertainty decomposition in spite of its usefulness. In this paper, we propose a novel method for decomposing the total uncertainties into model-wise uncertainties. The proposed model-wise uncertainty decomposition method can be applied with general uncertainty measures, which include mean absolute deviation and variance measures. Moreover, the proposed method provides an intuitive interpretation of the quantified model-wise uncertainties. The results of analyzing real data by the proposed method are presented. [ABSTRACT FROM AUTHOR]

This paper assesses the long-term combined effects of land use (LU) and climate change on river hydrology and water scarcity of two rivers of the Western Ghats of India. The historical LU changes were studied for four decades (1988–2016) using the maximum likelihood algorithm and the long-term LU (2016–2075) was estimated using the Dyna-CLUE prediction model. Five General Circulation Models (GCMs) were utilized to assess the effects of climate change (CC) and the Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the two river catchments. To characterize granular effects of LU and CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006–2040), mid-future (2041–2070), and far-future (2071–2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the present-day LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment. [ABSTRACT FROM AUTHOR]

This paper shows how low-impact development (LID) practices affect the resilience of stormwater drainage system under climate change scenarios. A rainfall-runoff model was calibrated and evaluated a in a tropical watershed located in Midwestern Brazil. An ensemble of 17 general circulation model outputs forced by Representative Concentration Pathways (RCP 4.5 and RCP 8.5) was used to create future climate change scenarios up to 2095. The LID efficiency was evaluated based on the runoff peak reduction and the resilience of stormwater drainage by means of a resilience index. Overall, LID combinations showed a reduction in runoff peak higher than 20%, and the best LID combination had a reduction of as much as 46%. This represents a significant improvement in the resilience against flooding in the study area. Therefore, the findings can contribute to the increase in widespread adoption of LID and can encourage decisionmakers to provide such practices for urban flood management. [ABSTRACT FROM AUTHOR]

This paper describes the main characteristics of CNRM‐CM6‐1, the fully coupled atmosphere‐ocean general circulation model of sixth generation jointly developed by Centre National de Recherches Météorologiques (CNRM) and Cerfacs for the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6). The paper provides a description of each component of CNRM‐CM6‐1, including the coupling method and the new online output software. We emphasize where model's components have been updated with respect to the former model version, CNRM‐CM5.1. In particular, we highlight major improvements in the representation of atmospheric and land processes. A particular attention has also been devoted to mass and energy conservation in the simulated climate system to limit long‐term drifts. The climate simulated by CNRM‐CM6‐1 is then evaluated using CMIP6 historical and Diagnostic, Evaluation and Characterization of Klima (DECK) experiments in comparison with CMIP5 CNRM‐CM5.1 equivalent experiments. Overall, the mean surface biases are of similar magnitude but with different spatial patterns. Deep ocean biases are generally reduced, whereas sea ice is too thin in the Arctic. Although the simulated climate variability remains roughly consistent with CNRM‐CM5.1, its sensitivity to rising CO2 has increased: the equilibrium climate sensitivity is 4.9 K, which is now close to the upper bound of the range estimated from CMIP5 models. Key Points: Description of CNRM‐CM6‐1 model components, their coupling, and tuning procedures are describedHistorical simulations and DECK experiments are assessedPreindustrial simulation is stable and mean climate and variability in historical runs is realistic [ABSTRACT FROM AUTHOR]

This paper studies the Upper Tigris River (UTR) drainage basin in Turkey for climate change impacted runoff estimations. Statistical downscaling method (SDM) is used by taking into consideration spatial dependence function (SDF) for the scenario precipitation projections at a set of available meteorology stations. Temporal adjustment between the climate scenarios and precipitation record time series is achieved by the white Markov (WM) stochastic process. Although various climate research center scenario data are considered, herein, only the general circulation model (GCM) A2 scenario data are adapted from the Hadley Center, England. The precipitation and runoff results are presented in decadal groups starting from 2001 to 2050 as cumulative monthly precipitation (CMP) and cumulative monthly runoff (CMR) graphs. It is observed that after 2021, precipitation decreases at about 12.5% and after 2030, it is 26%. Runoff projections indicate that they may decrease at about 30% especial after 2040. [ABSTRACT FROM AUTHOR]

The paper presents a new INM RAS atmospheric general circulation model, which includes troposphere, stratosphere, mesosphere, and the lower thermosphere, as well as the lower ionospheric regions (INMAIM). Based on the atmospheric part of the INM climatic model INMCM, a new general circulation model was created by adding the middle atmosphere and lower ionosphere description up to 130 km altitudes. A new computational unit for radiative processes calculation was developed for this purpose. For the lower ionosphere a separate plasma chemistry local model was created. The identification of the INMAIM model climate in the mesosphere and lower thermosphere was carried out based on climatological observations. It was shown that model reproduces the general climatic characteristics considerably well. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are routinely run under Atmospheric Modelling Intercomparison Project (AMIP) conditions with prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) from observations. These AMIP simulations are often used to evaluate the role of the land and/or atmosphere in causing the development of systematic errors in such GCMs. Extensions to the original AMIP experiment have also been developed to evaluate the response of the global climate to increased SSTs (prescribed) and carbon dioxide (CO2) as part of the Cloud Feedback Model Intercomparison Project (CFMIP). None of these international modelling initiatives has undertaken a set of experiments where the land conditions are also prescribed, which is the focus of the work presented in this paper. Experiments are performed initially with freely varying land conditions (surface temperature, and soil temperature and moisture) under five different configurations (AMIP, AMIP with uniform 4K added to SSTs, AMIP SST with quadrupled CO2, AMIP SST and quadrupled CO2 without the plant stomata response, and increasing the solar constant by 3.3%). Then, the land surface temperatures from the free land experiments are used to perform a set of "AMIP prescribed land" (PL) simulations, which are evaluated against their free land counterparts. The PL simulations agree well with the free land experiments, which indicates that the land surface is prescribed in a way that is consistent with the original free land configuration. Further experiments are also performed with different combinations of SSTs, CO2 concentrations, solar constant and land conditions. For example, SST and land conditions are used from the AMIP simulation with quadrupled CO2 in order to simulate the atmospheric response to increased CO2 concentrations without the surface temperature changing. The results of all these experiments have been made publicly available for further analysis. The main aims of this paper are to provide a description of the method used and an initial validation of these AMIP prescribed land experiments. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the improved weather generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulation run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of general circulation model (GCM) scenarios to assess regional climate change impacts, especially hydrological effects. [ABSTRACT FROM AUTHOR]

Previous studies have recognized the societal relevance of climatic extremes on the seasonal time scale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet orwarm seasons). However, these findings have not yet been generalized beyond case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogs to even themost extreme ERA-Interimevents. This approach offers numerous opportunities for systematically analyzing large samples of extreme seasons, and several such analyses are illustrated. We reveal a striking co-occurrence of ElNiño to La Niña transitions and the largest ERA-Interim midlatitude extreme summer events. Moreover, we perform a climate model evaluation with regard to extreme season size and intensitymeasures and estimate how often an extreme winter like the coldNorth American 2013/14 winter is expected anywhere inmidlatitude regions. Furthermore, we present a large set of simulated analogs to this event, whichmakes it possible to studycommonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified. [ABSTRACT FROM AUTHOR]

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require O(105) model runs and are sensitive to internal variability noise, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only O(102) model runs and can accommodate internal climate variability. The approach consists of three stages: (a) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (b) an emulation stage emulates the parameter‐to‐data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (c) a sampling stage approximates the Bayesian posterior distributions by sampling the GP emulator with MCMC. We demonstrate the feasibility and computational efficiency of this calibrate‐emulate‐sample (CES) approach in a perfect‐model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from synthetic data generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties. Plain Language Summary: Calibrating climate models with available data and quantifying their uncertainties are essential to make climate predictions accurate and actionable. A primary source of uncertainties in climate models comes from representation of small‐scale processes such as moist convection. Parameters in convection schemes and other parameterizations are usually calibrated by hand, using only a small fraction of data that are available. As a result, the calibration process may miss information about the small‐scale processes in question. This paper presents a proof‐of‐concept, in an idealized setting, of how parameters in climate models can be calibrated using a substantial fraction of the available data, and how uncertainties in the parameters can be quantified. We employ a new algorithm, called calibrate‐emulate‐sample (CES), which makes such calibration and uncertainty quantification feasible for computationally expensive climate models. CES reduces the hundreds of thousands of model runs usually required to quantify uncertainties in computer models to hundreds, thereby achieving about a factor 1,000 speedup. It leads to more robust calibration and uncertainty quantification in the presence of noise arising from chaotic variability of the climate system. We show how uncertainties in climate model parameters can be translated into quantified uncertainties of climate predictions through ensemble integrations. Key Points: We use time averaged climate statistics to calibrate convective parameters and quantify their uncertaintiesWe demonstrate use of the calibrate‐emulate‐sample algorithm to provide efficient calibration and uncertainty quantificationParametric uncertainty in climate predictions is quantified by sampling from the learnt convective parameter distribution [ABSTRACT FROM AUTHOR]

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]

This paper describes version 2.0 of the Global Change and Air Pollution (GCAP 2.0) model framework, a one-way offline coupling between version E2.1 of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) and the GEOS-Chem global 3-D chemical-transport model (CTM). Meteorology for driving GEOS-Chem has been archived from the E2.1 contributions to phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the pre-industrial era and the recent past. In addition, meteorology is available for the near future and end of the century for seven future scenarios ranging from extreme mitigation to extreme warming. Emissions and boundary conditions have been prepared for input to GEOS-Chem that are consistent with the CMIP6 experimental design. The model meteorology, emissions, transport, and chemistry are evaluated in the recent past and found to be largely consistent with GEOS-Chem driven by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) product and with observational constraints. [ABSTRACT FROM AUTHOR]

Observations of warm past climates and projections of future climate change show that the Arctic warms more than the global mean, particularly during winter months. Previous work has attributed this reduced Arctic land seasonality to the effects of sea ice or clouds. In this paper, we show that the reduced Arctic land seasonality is a robust consequence of the relatively small surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission, without recourse to other processes or feedbacks. We use a general circulation model (GCM) with no clouds or sea ice and a simple representation of land. In the annual mean, the equator-to-pole surface temperature gradient falls with increasing CO2, but this is only a near-surface phenomenon and is not caused by the change in total meridional heat transport, which is virtually unaltered. The high-latitude land has about twice as much warming in winter than in summer, whereas high-latitude ocean has very little seasonality in warming. A surface energy balance model shows how the combination of the smaller surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission gives rise to the reduced seasonality of the land surface. The increase in evaporation over land also leads to winter amplification of warming over land, although amplification still occurs without it. While changes in clouds, sea ice, and ocean heat transport undoubtedly play a role in high-latitude warming, these results show that enhanced land surface temperature warming in winter can happen in their absence for robust reasons. [ABSTRACT FROM AUTHOR]

Optimizing the parameters of a land surface process model (LSPM) through data assimilation (DA) can not only improve and perfect the parameterization schemes in the LSPM through the physical mechanism, but also increase its regional adaptability and simulation capability. This has practical importance for improving simulation results and the climate-prediction capability of general circulation models (GCMs) and regional climate models (RCMs). This paper presents a DA-based method for optimizing the parameterization schemes in LSPMs. We optimize the unsaturated-soil water flow (UnSWF) model as an example by developing a soil-moisture assimilation scheme based on the UnSWF model and the extended Kalman filter (EKF) algorithm, and then combining them with the Variable Infiltration Capacity (VIC) model. Using a month as the assimilation window, we used the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to minimize the objective function through simulated and assimilated soil moisture, achieved the best fit with the given objective function measurement, and optimized the parameters of the UnSWF model, including the saturated-soil hydraulic conductivity, moisture content, matrix potential, and the Clapp and Hornberger constant. The optimal values of the model parameters were obtained during the DA period (the year 1986), and then the optimized parameters were used to improve the UnSWF model. Finally, numerical simulation experiments were carried out from 1986 to 1993 to evaluate the simulation capability of the improved model and to explore and realize the DA-based method for optimizing the soil water parameterization scheme in LSPMs. The experimental results indicated that the optimized model parameters improved and perfected the model based on the physical mechanism, and increased its simulation capability; the optimized model parameters had good temporal portability and their adaptability was stronger, achieving the aim of improving the model. Therefore, this method is reasonable and feasible. This paper provides a good reference for DA-based optimization of the parameterization schemes in LSPMs. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on the impact of climate change on road infrastructure in South Africa. The approach used built on previous work associated with the UNU-WIDER Development under Climate Change (DUCC) effort, emphasizing understanding of the impact of climate change on roads. This paper illustrates how climate change effects on road infrastructure can be evaluated at a national and provincial level to produce quantitative estimates of climate change from multiple future scenarios. The results of the study indicate that the national-level climate change cost impact in South Africa will vary between US$116.8 million and US$228.7 million annually in the 2050 decade for the median and maximum climate scenarios if a no-adaptation policy action is taken. Conversely, if a proactive adaptation action is taken, these costs can be reduced to US$55.7 million. The savings in costs arising from climate change impacts based on this policy approach is equivalent to building more than 10,000 km of new, secondary paved road by 2050. The analysis focused on the advantage of a proactive policy approach: the upgrade of road infrastructure where climate impacts will exceed current design and maintenance standards. A stressor-response methodology created by the writers is briefly explained, including the application of 54 potential climate futures using general circulation models (GCMs) approved by the Intergovernmental Panel on Climate Change (IPCC). This paper focuses on provincial-level impacts of both a proactive and a reactive approach. Decadal and average annual costs are detailed through 2100. [ABSTRACT FROM AUTHOR]

Dynamics of the stratosphere and ozone layer are among important sources of atmospheric circulation predictability at subseasonal-to-seasonal time scales. The simulation of the stratospheric dynamics with the SL-AV atmospheric general circulation model for the SLAV072L96 seasonal weather prediction configuration is analyzed. The configuration is currently under preoperational testing at the Hydrometcenter of Russia. The model simulates both winter and summer averaged distributions of zonal wind and temperature close to the reanalysis data. The quasi-biennial oscillation of equatorial zonal wind is simulated with a realistic period and amplitude. There is a significant reduction of errors as compared with the previous stratosphere-resolving SL-AV model configuration. It is shown that the stratospheric process simulation enhancement is largely due to the reduction of systematic errors in the simulation of troposphere dynamics. The work on the inclusion of the CHARM photochemical model in the SL-AV model is described. The results of first experiments with the coupled model are given. [ABSTRACT FROM AUTHOR]

In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth's climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model (CESM). I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (MDE) and climate dynamic exploration (CDE). MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. [ABSTRACT FROM AUTHOR]

This paper applies the Multivariate Adaptive Constructed Analogs (MACA) statistical downscaling method to 12 general circulation models to produce 21st century projections of fire weather variables over Victoria, Australia, under two emissions scenarios. The statistically downscaled model data accurately replicate the observed distributions of meteorological variables over the contemporary period, but underestimate fire danger extremes in some models. Under each climate scenario, both mean and extreme fire danger are expected to increase. Though there is variation across Victoria, the 12-model average by year for RCP8.5 indicates a 10–20% increase in extreme (99th percentile) Forest Fire Danger Index across the state, with the greatest change projected in the north-west region. At five geographically and climatologically different locations across Victoria, there is a 50–200% increase in the number of days per year exceeding the threshold for the Victorian Very High or higher fire danger rating by the end of the century compared with the start. The high-end warming (RCP8.5) scenario shows increased temperature to be the main driver of heightened fire danger. Changes in temperature, humidity and precipitation during spring and early summer both increase the length of the fire season and may reduce springtime opportunities for prescribed burning. Twelve GCMs are downscaled over Victoria, Australia, to estimate trends in fire weather frequency under two emissions scenarios using the Multivariate Adaptive Constructed Analogs (MACA) method. Increases of ~50–200% in the number of Very High or above fire danger days are likely by 2100, mostly due to higher temperatures. Photo showing smoke from a planned burn in Victoria, Australia, by Nick McCarthy. [ABSTRACT FROM AUTHOR]

Over time scales between 10 days and 10–20 years—the macroweather regime—atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation, which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For 1 month lead time, our simple stochastic model shows similar—but somewhat higher—values of the skill scores than the much more complex deterministic models. [ABSTRACT FROM AUTHOR]

The boreal summer intraseasonal oscillation (BSISO) is among the most pronounced subseasonal variability in the tropics during boreal summer. Compared with its wintertime counterpart, the so-called Madden-Julian oscillation (MJO), the BSISO convection displays more complicated spatiotemporal evolution, characterized by northward propagation over the northern Indian Ocean and western North Pacific as well as eastward propagation along the equator. It exerts a strong effect on a broad range of tropical weather and climate phenomena, such as tropical cyclogenesis, monsoon onset, and active/break cycles, among others. Our fundamental understanding of the BSISO has steadily advanced: so far various aspects of the BSISO have been described, and several theories that aim to elucidate its northward propagation have been proposed. Yet, our skill to simulate the BSISO by general circulation models remains unsatisfactory, though it has been improved. This paper reviews some fundamental aspects of the BSISO from the viewpoint of observation, theory, and modeling. [ABSTRACT FROM AUTHOR]

This paper investigates climate change impacts on the hydrology and water quality of Brazilian semi-arid reservoirs. An integrated approach coupling climate, hydrological and water quality models was proposed. Five general circulation models (GCMs) and two future scenarios (SSP2-4.5 and SSP5-8.5) were used for the projections. The simulations showed that uncertainty in climate forecast significantly impacted hydrological and water quality outputs. Some scenarios revealed a strong decrease in streamflow and storage volume. On the other hand, 40% of multi-GCM projections indicated reservoir eutrophication. The impacts on hydrology and water quality under SSP5-8.5 were more significant than those under SSP2-4.5. The results also indicated that drought events may be prolonged under climate change, principally at the end of the 21st century for SSP5-8.5, and may significantly increase the total phosphorus concentration. This suggests that tropical semi-arid reservoirs could be more vulnerable to eutrophication in scenarios of climate change than those in other regions. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of a Wasserstein generative adversarial network to produce realistic weather situations when trained from the climate of a general circulation model (GCM). To do so, a convolutional neural network architecture is proposed for the generator and trained on a synthetic climate database, computed using a simple three dimensional climate model: PLASIM. The generator transforms a "latent space", defined by a 64-dimensional Gaussian distribution, into spatially defined anomalies on the same output grid as PLASIM. The analysis of the statistics in the leading empirical orthogonal functions shows that the generator is able to reproduce many aspects of the multivariate distribution of the synthetic climate. Moreover, generated states reproduce the leading geostrophic balance present in the atmosphere. The ability to represent the climate state in a compact, dense and potentially nonlinear latent space opens new perspectives in the analysis and handling of the climate. This contribution discusses the exploration of the extremes close to a given state and how to connect two realistic weather situations with this approach. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the level of description of the main physical processes in the Earth's atmosphere in modern models of the general circulation of the Earth's atmosphere and gives a brief overview of the modern models used by the main forecasting centers. The promising directions of the development of models of the general circulation of the Earth's atmosphere are discussed. [ABSTRACT FROM AUTHOR]

The reliability of general circulation models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate, and thus the evaluation of GCM performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCM performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included. A new methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs' abilities to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particular GCM is compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and for eight subcontinental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the Southern Hemisphere. However, GCMs tend to overestimate the warming rate for domains in the Northern Hemisphere, particularly since the mid-1990s. [ABSTRACT FROM AUTHOR]

The electromagnetic (EM) field variations capable of probing the electrical conductivity of the upper mantle and mantle transition zone have a period range between a few hours and 1 day. At these periods, the dominant source of the EM signals is the ionospheric current system, which has a complex spatial and temporal structure. A concept of global-to-local (G2L) transfer functions can handle spatially complex source by relating global source expansion coefficients with locally measured magnetic (or/and electric) fields. When estimating the G2L transfer functions, the source is commonly expanded into spherical harmonics (SH). In this paper, we explore an alternative parametrization of the source based on a principal component analysis (PCA) of the Fourier transformed output from the physics-based Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM). Specifically, we investigate whether magnetic fields computed in the realistic three-dimensional conductivity model of Earth excited by the PCA-based source agree better with observatory data than those computed in the same model but induced by the SH-based source. Using PCA to capture the source current compared to SH parametrization, we find that agreement with the observatory data is better during magnetically disturbed times and at shorter periods. Vice versa, it is poorer during magnetically quiet times and at longer periods. [ABSTRACT FROM AUTHOR]

This paper attempts to enhance our understanding of the causes of Atlantic multidecadal variability (AMV). Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both) and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the twentieth-century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, while the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations. [ABSTRACT FROM AUTHOR]

Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. While the country-level scale of emissions changes can be estimated in near real time, the more detailed, gridded emissions estimates that are required to run general circulation models (GCMs) of the climate will take longer to collect. In this paper we use recorded and projected country-and-sector activity levels to modify gridded predictions from the MESSAGE-GLOBIOM SSP2-4.5 scenario. We provide updated projections for concentrations of greenhouse gases, emissions fields for aerosols, and precursors and the ozone and optical properties that result from this. The code base to perform similar modifications to other scenarios is also provided. We outline the means by which these results may be used in a model intercomparison project (CovidMIP) to investigate the impact of national lockdown measures on climate, including regional temperature, precipitation, and circulation changes. This includes three strands: an assessment of short-term effects (5-year period) and of longer-term effects (30 years) and an investigation into the separate effects of changes in emissions of greenhouse gases and aerosols. This last strand supports the possible attribution of observed changes in the climate system; hence these simulations will also form part of the Detection and Attribution Model Intercomparison Project (DAMIP). [ABSTRACT FROM AUTHOR]

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climate prediction.net (https://www.climateprediction.net/ , last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events. [ABSTRACT FROM AUTHOR]

Conventional long‐range weather prediction is an initial value problem that uses the current state of the atmosphere to produce ensemble forecasts. Purely stochastic predictions for long‐memory processes are "past value" problems that use historical data to provide conditional forecasts. Teleconnection patterns, defined from cross‐correlations, are important for identifying possible dynamical interactions, but they do not necessarily imply causation. Using the precise notion of Granger causality, we show that for long‐range stochastic temperature forecasts, the cross‐correlations are only relevant at the level of the innovations–not temperatures. This justifies the Stochastic Seasonal to Interannual Prediction System (StocSIPS) that is based on a (long memory) fractional Gaussian noise model. Extended here to the multivariate case (m‐StocSIPS) produces realistic space‐time temperature simulations. Although it has no Granger causality, emergent properties include realistic teleconnection networks and El Niño events and indices. Plain Language Summary: For forecasts less than about 10 days, Numerical Weather Prediction (NWP) and General Circulation Models (GCMs) have been highly successful, yet for longer ranges, their skill is disappointing. This has motivated the development of alternatives that are based on either the strong spatial correlations–teleconnection patterns such El Niño events–or on the long memories whereby the atmospheric state at any moment is strongly influenced by its own past. In particular, a model only using the long memory already rivals GCM monthly and seasonal temperature forecasts: The Stochastic Seasonal to Interannual Prediction System (StocSIPS). In this paper, we answer the question of whether StocSIPS skill can be further improved by using teleconnections. We do this by developing the space‐time m‐StocSIPS model that is optimally forecast by StocSIPS. In m‐StocSIPS, spatial co‐predictors do not improve the skill: There is no causal relation between different locations useful for long‐range predictions. Although m‐StocSIPS has strong spatial correlations and reproduces teleconnection patterns including El Niño events, they cannot be used to improve the long‐memory StocSIPS forecasts. The teleconnections "were already used" to build the history at every location, which is enough to produce the optimal forecast. Key Points: Granger causality shows that spatial correlations in temperature cannot be used to improve on memory‐based predictions of infinitely long seriesScaling‐based long‐range stochastic forecasting is a past value problem not an initial value problemReal‐world statistics and teleconnection patterns can be reproduced with stochastic simulations with a total lack of causal relationships [ABSTRACT FROM AUTHOR]

This paper presents new laboratory-scale numerical simulations of density-driven exchange flows generated across an idealised, submerged sill obstruction under both non-rotating and rotating frames of reference using the Bergen Ocean Model (BOM), a three-dimensional general ocean circulation model. Initial non-rotating BOM simulations are compared directly with previous laboratory data obtained in a large-scale channel facility incorporating an idealised trapezoidal sill. These laboratory experiments demonstrate that the saline intrusion flux across the sill is initially reduced and then eventually fully blocked under increasing net-barotropic flow conditions imposed in the counterflowing upper freshwater layer, with the saline blockage also more evident for reduced sill submergence depths. These parametric dependences are also demonstrated in the equivalent BOM simulations of the non-rotating sill exchange flows, although the numerical model results tend to overpredict both the interfacial velocity and density gradients across the sill (as indicative of suppressed interfacial mixing), as well as the fresh-saline source flux ratio at which full blockage of the saline intrusion occurs. The BOM simulations are then extended to consider rotating sill exchange flow dynamics. In particular, these additional runs demonstrate that Coriolis forces increase the overall blockage of the saline intrusion layer compared to equivalent non-rotating exchange flows, especially when the Rossby number associated with the saline intrusion flow across the sill is considerably less than unity. This effect is largely attributed to the development of Ekman boundary layer dynamics and associated secondary circulations within the bi-directional exchange flows. These are shown to impose strong control on the transverse distribution and extent of the lower saline intrusion flow across the sill and, hence, the parametric conditions under which full saline intrusion blockage is achieved in rotating sill exchange flows. [ABSTRACT FROM AUTHOR]

The westerly jet (WJ) is an important component of atmospheric circulation, which is characterized by prominent seasonal variations in intensity and position. However, the response of the WJ over Asia during the Last Glacial Maximum (LGM) is still not clear. Using general circulation model experiments, the seasonal behaviors of the WJ over central Asia and Japan are analyzed in this paper. The results show that, compared to the present day (PD), the WJ presents a complicated response during the LGM, both in intensity and position. Over central Asia, it becomes weaker in both summer and winter. But over Japan, it is enhanced in summer but becomes diminished in winter. In terms of position, the WJ over central Asia shifts southward in both summer and winter, whereas the WJ over Japan moves southward in summer but does not change obviously relative to PD in winter. Such WJ changes are well explained by meridional temperature gradients in high troposphere, which is closely linked to seasonal thermal anomalies over the Tibetan Plateau (TP). Despite cooler LGM conditions, the anomalous warming center over the TP becomes stronger in summer. Derived from the heat budget equation, the stronger heating center is mainly caused by the weaker adiabatic cooling generated from ascending motion over the area south of the TP. In winter, the cooling over the TP is also strengthened, mostly owing to the subsidence-induced weaker adiabatic heating. Due to the importance of the WJ, the potential role of TP thermal effects should be a focus when explaining the East Asian climate change during the LGM. [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]

Assessing the impact of climate change on water demand is a challenging task. This paper proposes a novel methodology that quantifies this impact by establishing a link between water demand and weather based on climate change scenarios, via Coupled General Circulation Models. These models simulate the response of the global climate system to increasing greenhouse gas concentrations by reproducing atmospheric and ocean processes. In order to establish the link between water demand and weather, Random Forest models based on weather variables were used. This methodology was applied to a district metered area in Naples (Italy). Results demonstrate that the total district water demand may increase by 9–10% during the weeks with the highest temperatures. Furthermore, results show that the increase in water demand changes depending on the social characteristics of the users. The water demand of employed users with high education may increase by 13–15% when the highest temperatures occur. These increases can seriously affect the capacity and operation of existing water systems. [ABSTRACT FROM AUTHOR]

This paper concerns the simulation of the water table elevation in shallow unconfined aquifers where infiltration is assumed as the main mechanism of recharge. The main aim is to provide a reliable tool for groundwater management that satisfies water supply managers. Such a tool is a candidate as a physically based alternative to the use of empirical methods or general circulation models. It is based on the use of two widely available sets of data: the water table elevation measurements and soil moisture time series. In fact, the former are usually provided by government agencies on public websites whereas the latter are included in the atmospheric global datasets (reanalysis). It is notable that data from reanalysis are accessible to any citizen and organization around the world on an open-access basis (e.g., Copernicus). In the proposed method, the measured water table elevations are correlated quantitatively with the water fluxes toward the aquifer evaluated using the soil moisture data from ERA5 reanalysis (provided by ECMWF) within a Richards equation-based approach. The analysis is executed using data from the Umbria region (Italy) on both a daily and monthly scale. In fact, these are the time intervals of interest for a proper management of groundwater resources. The proposed relationships include both a logarithmic and linear term and point out the possible different regimes of the shallow aquifers with regard to the recharge due to infiltration. These different mechanisms reflect in the different role played by the water fluxes toward the aquifer in terms of water table elevation changes according to the considered time scale. [ABSTRACT FROM AUTHOR]

This paper describes and analyzes the Reed-Jablonowski (RJ) tropical cyclone (TC) test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). The intermediate complexity test case analyzes the evolution of a weak vortex into a TC in an idealized tropical environment. Simulations from general circulation models (GCMs) that participated in DCMIP2016 are analyzed in this study at 50 km horizontal grid spacing, with 5 of these models also providing simulations at 25 5 km grid spacing for an analysis on the impact of finer grid spacing. Evolution of minimum surface pressure (MSP) and maximum 1 km azimuthally averaged wind speed (MWS), the wind-pressure relationship, radial profiles of wind speed and surface pressure, and wind composites are documented for all participating GCMs at both horizontal grid spacings. While results are generally similar between all models, some GCMs reach significantly higher storm intensities than others, ultimately impacting specific characteristics of their horizontal and vertical structure. TCs simulated at 25 km grid spacings retained these differences, but reach higher intensities and are more compact than their 50 km counterparts. These results indicate dynamical core choice is an essential factor in GCM development, and future work should be conducted to explore how specific differences within the dynamical core affect TC behavior in GCMs. [ABSTRACT FROM AUTHOR]

Knowledge of the daytime variability of cloud fraction is pivotal for the accurate determination of the atmosphere's energy balance. Yet, polar orbiting satellites are unable to fully capture daytime variability and individual geostationary satellites only provide measurements for portions of the Earth. In addition, diurnal cloud fraction variations analyzed with General Circulation Models provide results that cannot be reconciled with observations in many situations. Here, we show that the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) satellite presents a unique opportunity to examine changes in daytime global cloud fraction resolved at one‐hour frequency. For the first time, we characterize the global daytime variability of clouds with a single sensor, presenting evidence that the fraction of liquid clouds reaches its maximum over continental areas and its minimum over ocean around noon. We compare our results with previous global and regional studies and examine implications for the Earth's energy budget. Plain Language Summary: Cloud coverage changes during the day over every location of the Earth. While those movements might seem random for a particular day, patterns emerge when cloud fraction is evaluated with sufficient data in local times. The Earth Polychromatic Imaging Camera (EPIC) aboard the DSCOVR satellite observes the Earth from approximately 1.5 million kilometers away, capturing the whole sunlit side of the planet at once. Therefore, each one of EPIC's images contains information about daytime cloud fraction at different local times, with early morning and late afternoon in the left and right edges respectively. As EPIC takes between 10‐22 images per day, we can track the daytime evolution of cloud fraction with a single sensor. In this paper, we aggregate seasonally four years of data to show that there are more liquid clouds over land and less over ocean around noon. On the contrary, ice clouds evolve independently of the underlying surface type, with larger cloud fraction in the morning and afternoon and a minimum at noon. Key Points: Global daytime cycles are analyzed with a single sensor for the first time using DSCOVR/EPICLiquid cloud fraction is maximum over land and minimum over ocean around noonIce cloud fraction is higher in the morning and afternoon than around noon independently of the surface type [ABSTRACT FROM AUTHOR]

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

The responses of North American precipitation to the Atlantic warm pool (AWP) are investigated by using observational data and the NCAR Community Earth System Model. We show that the responses are controlled by different physical mechanisms in different seasons. In the warm season, a large AWP reduces the North Atlantic subtropical high, consistent with Gill's physics. The corresponding influence over North America is northerly wind anomalies in the lower atmosphere, which leads to a precipitation suppression in the central United States. However, in the cold season the AWP's impact on North American precipitation is operated via the teleconnection of cold SST anomalies in the tropical Pacific which are induced by the AWP. A large AWP enhances the local Hadley circulation that moves across the equator to the tropical southeastern Pacific in the boreal summer. This inter-hemispheric process strengthens the atmospheric sinking and then increases the South Pacific subtropical high, resulting in the enhancement of the surface easterly trade wind and thus cold SST anomalies in tropical southeastern Pacific. The wind-evaporation-SST and Bjerknes feedbacks further lead tropical central Pacific SST to a La Niña-like pattern, which is consistent with that the significant and negative correlation between ENSO and the AWP is observed after the 1990s when the AWP leads by 6–9 months. The AWP-induced La Niña-like SST anomalies in the tropical central Pacific further prompts a negative phase of the Pacific North American teleconnection, resulting in decreased precipitation over the southern United States; and vice versa. In addition to the AWP-induced central Pacific-type of ENSO events, the paper also shows the influences of the AWP on SSTs in the North Pacific and Indian Oceans. [ABSTRACT FROM AUTHOR]