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This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

Precipitation is highly variable on spatial scales smaller than a typical general circulation model (GCM) grid box. Neglecting this subgrid‐scale variability can impact the simulation of the underlying land surface and its coupling with the atmosphere. In this study we present a scheme to stochastically disaggregate precipitation from an atmospheric grid to an underlying mosaic of surface tiles, using an assumed probability distribution of subgrid precipitation derived from observations. Unlike previous GCM‐based schemes, our approach includes a treatment of memory to allow persistence of subgrid features. In single column model experiments, we demonstrate the ability of the scheme to reproduce observed precipitation statistics at the spatial scale of the surface tiles. The root mean square error of hourly spatial standard deviation of precipitation is reduced from 1.90 to 0.96 mm hr−1 when disaggregation is applied. The scheme increases the spatial standard deviations of surface heat fluxes, soil moisture and temperature, and we show that incorporating memory amplifies these increases by a factor of 2–4. We also document increases in mean precipitation runoff and the Bowen ratio. Plain Language Summary: Global climate and weather models (GCMs), used for global warming projections and near‐term weather prediction, represent the atmosphere on a grid, while the underlying land surface is often composed of smaller elements. In most models, all surface elements under a given atmospheric grid point receive the same precipitation rate. However, real‐world precipitation rates vary significantly over the scale of a GCM grid cell. This paper presents an approach to represent subgrid precipitation variability in a GCM. Grid mean precipitation is randomly distributed across surface elements, such that surface precipitation reproduces observed statistics. The approach is found to increase grid cell‐averaged surface runoff as well as the spatial variances of surface temperature, soil moisture, and surface sensible heat and moisture fluxes. Key Points: A parameterization is developed to stochastically distribute precipitation across subgrid surface tiles within a general circulation model (GCM)In single column experiments, the scheme reproduces observed precipitation statistics at the scale of land surface tilesThe scheme increases surface runoff, Bowen ratio, and the spatial variance of surface properties at the ARM Southern Great Plains site [ABSTRACT FROM AUTHOR]

The Philippines is highly vulnerable to multiple climate-related hazards due to its geographical location and weak adaptation measures. Floods are the most catastrophic hazards that impact lives, livelihoods, and, consequently, the economy at large. Understanding the ability of the general circulation models to simulate the observed rainfall using the latest state-of-the-art model is essential for reliable forecasting. Based on this background, this paper objectively aims at assessing and ranking the capabilities of the recent Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed rainfall over the Philippines. The Global Precipitation Climatology Project (GPCP) v2.3 was used as a proxy to gauge the performance of 11 CMIP6 models in simulating the annual and rainy-season rainfall during 1980–2014. Several statistical metrics (mean, standard deviation, normalized root means square error, percentage bias, Pearson correlation coefficient, Mann–Kendall test, Theil–Sen slope estimator, and skill score) and geospatial measures were assessed. The results show that that CMIP6 historical simulations exhibit satisfactory effectiveness in simulating the annual cycle, though some models display wet/dry biases. The CMIP6 models generally underestimate rainfall on the land but overestimate it over the ocean. The trend analysis shows that rainfall over the country is insignificantly increasing both annually and during the rainy seasons. Notably, most of the models could correctly simulate the trend sign but over/underestimate the magnitude. The CMIP6 historical rainfall simulating models significantly agree on simulating the mean annual cycle but diverge in temporal ability simulation. The performance of the models remarkably differs from one metric to another and among different time scales. Nevertheless, the models may be ranked from the best to the least best at simulating the Philippines' rainfall in the order GFDL, NOR, ACCESS, ENS, MRI, CMCC, NESM, FIO, MIROC, CESM, TAI, and CAN. The findings of this study form a good basis for the selection of models to be used in robust future climate projection and impact studies regarding the Philippines. The climate model developers may use the documented shortcoming of these models and improve their physical parametrization for better performance in the future. [ABSTRACT FROM AUTHOR]

Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, and infrastructure throughout the world. The intensity and frequency of this extreme weather event are expected to increase due to significant changes in the present‐day climate and land use and land cover (LULC) pattern. India has a very systematic and organized structural program and policies but lacks proper implementations, and adverse effect of climate change and the extreme event goes on in society. This paper is an analysis of floods in India and hazards due to climate change and LULC change patterns. Three models, namely "Eco‐biogeography‐based optimization (EBO), Random forest (RF), and Support vector machine (SVM)" were used to obtain the final output to prepare a "Flood susceptibility map". The result was validated through the "Receiver operating characteristics (ROC)" with "Area under curve (AUC)" values. The future rainfall scenario has been estimated by considering the "General circulation models" through different "shared socioeconomic pathways (SSPs)". The values of AUC are 0.915 (EBO), 0.887 (RF), and 0.869 (SVM), respectively. After consideration of different SSPs, the result shows that there is an increasing tendency of flood hazards in the projected period. Among all the employed modelling approaches, the EBO model has notable potential in delineating the possible flood‐prone regions for effective flood planning and management. Decision‐makers can benefit from country‐specific information and regional planner to implement sustainable and long‐term measures to overcome this type of hazardous situation. [ABSTRACT FROM AUTHOR]

This paper evaluates barotropic tides simulated by a newly developed multi-layer ocean general circulation, ICON-O, and assesses processes and model configurations that can impact the quality of the simulated tides. Such an investigation is crucial for applications addressing internal tides that are much more difficult to evaluate than the barotropic tides. Although not specially tuned for tides and not constrained by any observations, ICON-O is capable of producing the main features of the open-ocean barotropic tides as described by the geographical distributions of amplitude, phase, and amphidromic points. An error analysis shows, however, that the open-ocean tides simulated by ICON-O are less accurate than those simulated by two other ocean general circulation models (OGCMs), especially when not properly adjusting the time step and the parameters used in the time-stepping scheme. Based on a suite of tidal experiments, we show that an increase in horizontal resolution only improves tides in shallow waters. Relevant for using ICON-O with its telescoping grid capacity, we show that spatial inhomogeneity does not deteriorate the quality of the simulated tides. We further show that implementing a parameterization of topographic wave drag improves the quality of the simulated tides in deep ocean independent of the model configuration used, whereas the implementation of a self-attraction and loading (SAL) parameterization in a low-resolution (40 km) version of ICON-O degrades the quality of tides in shallow ocean. Finally, we show that the quality of tides simulated by ICON-O with low resolution (40 km) can be significantly improved by adjusting the time step or the parameters in the time-stepping scheme used for obtaining the model solution. [ABSTRACT FROM AUTHOR]

In this paper we describe the implementation of the carbon isotopes 13C and 14C (radiocarbon) into the marine biogeochemistry model REcoM3. The implementation is tested in long-term equilibrium simulations where REcoM3 is coupled with the ocean general circulation model FESOM2.1, applying a low-resolution configuration and idealized climate forcing. Focusing on the carbon-isotopic composition of dissolved inorganic carbon (δ13CDIC and Δ14CDIC), our model results are largely consistent with reconstructions for the pre-anthropogenic period. Our simulations also exhibit discrepancies, e.g., in upwelling regions and the interior of the North Pacific. Some of these differences are due to the limitations of our ocean circulation model setup which results in a rather shallow meridional overturning circulation. We additionally study the accuracy of two simplified modelling approaches for dissolved inorganic 14C, which are faster (15 % and about a factor of five, respectively) than the complete consideration of the marine radiocarbon cycle. The accuracy of both simplified approaches is better than 5 % which should be sufficient for most studies of Δ14CDIC. [ABSTRACT FROM AUTHOR]

We introduce ModE-Sim (Modern Era SIMulations), a medium-sized ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR (low-resolution) version (T63; approx. 1.8 ∘ horizontal grid width with 47 vertical levels). At the lower boundary we use prescribed sea surface temperatures and sea ice that reflect observed values while accounting for uncertainties in these. Furthermore we use radiative forcings that also reflect observed values while accounting for uncertainties in the timing and strength of volcanic eruptions. The simulations cover the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009, ModE-Sim consists of 31 620 simulated years in total. ModE-Sim is suitable for many applications as its various subsets can be used as initial-condition and boundary-condition ensembles to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation, mainly focusing on the two key variables, 2 m temperature and precipitation. We demonstrate ModE-Sim's ability to represent their mean state, to produce a reasonable response to external forcings, and to sample internal variability. Through the example of heat waves, we show that the ensemble is even capable of capturing certain types of extreme events. [ABSTRACT FROM AUTHOR]

In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real‐world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. Different optimization tools have been successfully applied to address this task of model calibration. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real‐world processes which cannot be fully resolved, it might be preferable to consider associated model parameters as random parameters. In this paper, a random parameter is drawn from a wide probability distribution for every singe model simulation. We developed an optimization approach that allows us to declare certain parameters random while optimizing those that are assumed to take fixed values. We designed a corresponding variant of the well known Covariance Matrix Adaption Evolution Strategy (CMA‐ES). The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA‐ES, our new method converges to a solution that better suits the credible range of the corresponding random parameter with less computational effort. Plain Language Summary: In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real‐world processes. The problem is commonly addressed by adapting parameter values to reach a good match between model simulations and corresponding observations. However, seeking one best value for every single model parameter might not always be optimal. For example, if model equations integrate over multiple real‐world processes it might be preferable to consider associated model parameters as random parameters that are drawn from a wide probability distribution for every single model simulation. To explore this further, we modified a popular variant of the Covariance Matrix Adaption Evolution Strategy (CMA‐ES) in order to tune the other parameters. The new algorithm was applied to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry. Compared to the deterministic CMA‐ES our new method converges to a solution that better suits the credible range of the corresponding random parameter. Key Points: Model calibration is an important task in the view of parametric uncertaintiesWe allow specific uncertain model parameters to be random while optimizing other model parameters, providing an efficient tool for the taskWe apply it to a global biogeochemical circulation model to quantify the impact of zooplankton mortality on the underlying biogeochemistry [ABSTRACT FROM AUTHOR]

Tidal signatures in sporadic E (Es) layer have been confirmed by observations and simulations. However, the effect of gravity waves (GWs) on the Es layer formation process has not yet been fully understood. In this paper, the modulation of Es layers by GWs is examined through numerical simulations, in which a physics‐based model of Es layer is forced by neutral winds from the High Altitude Mechanistic General Circulation Model that can resolve GWs with horizontal wavelengths longer than 156 km (λh > 156 km). Comparison of the simulation results with and without the GWs (1,350 km > λh > 156 km) forcing reveals that the inclusion of GWs leads to short‐period (1.2–3 hr) density perturbations in Es layers, which are also seen in ground‐based ionosonde observations. At a given time, the metallic ion density at altitudes between 120 and 150 km can either increase (by up to ∼+600%) or reduce (by up to −90%) in response to GW forcing. The relative density perturbations are smaller (by up to 60%) between 90 and 120 km altitude. It is also found that the GW effect on the metallic ion density relates to the longitude, which is mostly explained by the geographical distribution of GWs activity in the mesosphere and lower thermosphere region. The longitudinal variation of the background geomagnetic field plays only a secondary role. Key Points: The modulation of Es layers by gravity waves (GWs) (1,350 km > λh > 156 km) is simulatedGWs cause short‐period (1.2–3 hr) Fe+ density perturbations, leading to production of fine structure in mid‐latitude Es layersLongitudinal dependence in Fe+ density perturbations is mainly explained by the longitudinal distribution of GWs activity in the mesosphere and lower thermosphere region [ABSTRACT FROM AUTHOR]

Climate is closely related to human life, food security and ecosystems. Forecasting future climate provides important information for agricultural production, water resources management and so on. In this paper, historical climate data from 1962–2001 was used at three sites in Tianjin Baodi, Tianjin and Tanggu districts as baseline and the model parameters were calibrated by the Long Ashton Research Station Weather Generator (LARS-WG). 2m-temperatures in 2011–2020 were verified under two scenarios, representative concentration pathway (RCP) 4.5 and RCP8.5 in different atmospheric circulation models with optimal minimum 2m-temperatures at the three sites. From 2031–2050, Tianjin will be using more moderate minimum 2m-temperatures in future simulations. Support vector machines (SVM) were used to optimize the simulated data to obtain more accurate future maximum and minimum 2m-temperatures for the three sites. The results showed that the determinant coefficient of LARS-WG simulation was 0.8 and SVM optimized determinant coefficient was 0.9 which greatly improved the prediction accuracy. The minimum and maximum future 2m-temperatures optimized under European Community Earth System Model (EC-EARTH) were relatively low and the same future 2m-temperatures optimized under Hadley Centre Global Environment Model Earth System (Had-GEM2-ES4) were high especially in the RCP8.5 scenario which simulated 2051–2070 climate. The SVM optimization showed that the maximum and minimum 2m-temperatures were in general agreement with the original simulation values. [ABSTRACT FROM AUTHOR]

The potential role of the New England seamount chain (NESC) on the Gulf Stream pathway and variability has been long recognized, and the series of numerical experiments presented in this paper further emphasize the importance of properly resolving the NESC when modeling the Gulf Stream. The NESC has a strong impact on the Gulf Stream pathway and variability, as demonstrated by comparison experiments with and without the NESC. With the NESC removed from the model bathymetry, the Gulf Stream remains a stable coherent jet much farther east than in the experiment with the NESC. The NESC is the leading factor destabilizing the Gulf Stream and, when it is not properly resolved by the model's grid, its impact on the Gulf Stream's pathway and variability is surprisingly large. A high-resolution bathymetry, which better resolves the New England seamounts (i.e., narrower and rising higher in the water column), leads to a tighter Gulf Stream mean path that better agrees with the observed path and a sea surface height variability distribution that is in excellent agreement with the observations. [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

The SOLA Award in 2022 was given to two outstanding papers. The first paper, by Dr. Yasumitsu Maejima et al., focused on the potential impact of a rich phased array weather radar (PAWR) network on the numerical weather prediction (NWP) of a heavy rainfall event in Kyushu, Japan. The study showed that assimilating PAWR data improved heavy rainfall prediction up to a 1-hour lead time, suggesting that a network of fast-scanning radars combined with advanced data assimilation can lead to improved forecasts of local heavy rainfall systems. The second paper, by Dr. Tomoe Nasuno et al., examined the impact of mid-latitude sea surface temperature (SST) anomaly on tropical cyclone (TC) activity. The study demonstrated that the mid-latitude SST anomaly in the western North Pacific during the boreal summer of 2018 systematically affected TC activity and contributed to an increase in TC track density in the eastern North Pacific. This study provides new insights into the interaction between large-scale atmospheric circulation and TC activity. [Extracted from the article]

This paper describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement: This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. [ABSTRACT FROM AUTHOR]

This paper describes an implementation of the combined hybrid‐parallel prediction (CHyPP) approach of Wikner et al. (2020), https://doi.org/10.1063/5.0005541 on a low‐resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics‐based numerical model of a dynamical system (e.g., the atmosphere) with a computationally efficient type of machine learning (ML) called reservoir computing to construct a hybrid model. This hybrid atmospheric model produces more accurate forecasts of most atmospheric state variables than the host AGCM for the first 7–8 forecast days, and for even longer times for the temperature and humidity near the earth's surface. It also produces more accurate forecasts than a model based only on ML, or a model that combines linear regression, rather than ML, with the AGCM. The potential of the CHyPP approach for climate research is demonstrated by a 10‐year long hybrid model simulation of the atmospheric general circulation, which shows that the hybrid model can simulate the general circulation with substantially smaller systematic errors and more realistic variability than the host AGCM. Plain Language Summary: This paper presents a computationally efficient novel approach to construct a hybrid model of the atmosphere by combining a physics‐based model of the global atmospheric circulation with a machine learning component. The primary purpose of the hybrid model is to produce quantitative weather forecasts on the same grid as the physics‐based model. It is found that the hybrid model produces more accurate forecasts than the host physics‐based model for the first 7–8 forecast days for most forecast variables, and for even longer times for the temperature and humidity near the Earth's surface. Furthermore, the hybrid model is found to simulate the climate with substantially smaller systematic errors and more realistic temporal variability than the host model. Key Points: A hybrid model incorporating machine learning produces more accurate forecasts and more realistic climate than the host physics‐based modelThe hybrid model states are more realistically balanced and have substantially lower biases than the host modelThe hybrid model produces more realistic atmospheric variability than the host model at time scales shorter than about a week [ABSTRACT FROM AUTHOR]

Sub-Neptunes (planets with radii between 2 and 4 R ⊕) are abundant around M-dwarf stars, yet the atmospheric dynamics of these planets is relatively unexplored. In this paper, we aim to provide a basic underpinning of the dry dynamics of general low-mean-molecular-weight, temperate sub-Neptune atmospheres. We use the ExoFMS general circulation model (GCM) with an idealized gray-gas radiation scheme to simulate planetary atmospheres with different levels of instellation and rotation rates, using the atmosphere of K2-18b as our control. We find that the atmospheres of tidally locked (TL), temperate sub-Neptunes have weak horizontal temperature gradients owing to their slow rotation rates and hydrogen-dominated composition. The zonal wind structure is dominated by high-latitude cyclostrophic jets driven by the conservation of angular momentum. At low pressures we observe superrotating equatorial jets, which we propose are driven by a Rossbyâ€"Kelvin instability similar to the type seen in simulations of idealized atmospheres with axisymmetric forcing. By viewing the flow in TL coordinates, we find the predominant overturning circulation to be between the day side and night side, and we derive scaling relations linking the TL stream function and vertical velocities to instellation. Comparing our results to the only other GCM study of K2-18b, we find significant qualitative differences in dynamics, highlighting the need for further collaboration and investigation into the effects of different dynamical cores and physical parameterizations. This paper provides a baseline for studying the dry dynamics of temperate sub-Neptunes, which will be built on in part II with the introduction of moist effects. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "A seasonal undercurrent along the northwest coast of Australia." The author acknowledges an error in the analytic solutions and figures presented in the original article. The corrected solutions and figures have been provided, and the author states that this error does not affect the scientific conclusions of the article. The corrigendum was published in the journal Frontiers in Marine Science. [Extracted from the article]

Philosophers have yet to provide a systematic analysis of the relationship between historical science and model-based science. In this paper I argue that prototypical model-based sciences exhibit features understood to be central to historical science. Philosophers of science have argued that historical scientists are distinctly concerned with inference to the best explanation, that explanations in historical science tend to increase in complexity over time, and that the explanations take the form of narratives. Using general circulation models in climate science as a reference, I illustrate how simulation models in model-based science share these features exhibited by historical science. That model based sciences share these features raises important philosophical questions about how we should understand prototypical types of scientific enquiry, including the relationship between experimental science, historical science, and model-based science. I conclude by exploring several options for how to accommodate the noted similarities within a more general taxonomy of the sciences. [ABSTRACT FROM AUTHOR]

When solar wind and interplanetary magnetic field (IMF) disturb, thermospheric winds change accordingly. Among the momentum forces driving high‐latitude thermospheric winds, ion drag is supposed to greatly affect wind variations through ion‐neutral coupling when abrupt and strong changes in ion drifts occur. However, due to the great inertia of thermospheric winds it needs a certain period of time for the wind changes to be prominent both in speed and direction. How long the neutral winds take to change from one steady state to another through the ion‐neutral coupling process is currently still a controversial issue. In this paper, we examine the high latitudinal ion‐neutral coupling time scale based on the Thermosphere Ionosphere Electrodynamics General Circulation Model simulations, which can determine whether wind variations are dominantly driven by ion drag by analyzing the relative contribution of each momentum force. It is found that the spatial variation of ion‐neutral coupling time scale is primarily determined by local electron density, but also varies with neutral density and ion‐neutral collision frequency. Simulations during periods of medium solar activity at ∼250 km altitude show that the ion drag‐dominated region is generally located at the dayside convection inverse boundary and the coupling time scale (e‐folding time) is ∼1 hr when IMF By is the dominant component of the IMF and changes direction. Meanwhile, the southward component of IMF Bz enlarges the ion drag‐dominated region. When IMF Bz is southward with a large magnitude, ion drag‐dominated region is primarily located in the nightside auroral oval with ∼2 hr coupling time scale. Key Points: The spatial variation of ion‐neutral coupling time scale depends primarily on local electron densityThe ion drag dominant region is located at the dayside convection cell boundary (nightside auroral oval) when interplanetary magnetic field (IMF) By (negative Bz) disturbsWhen wind variation is greatly affected by IMF By, the concurrent negative shift of IMF Bz obviously enlarges the ion drag dominant region [ABSTRACT FROM AUTHOR]

This paper introduces the new Venus global ionosphere‐thermosphere model (V‐GITM) which incorporates the terrestrial GITM framework with Venus‐specific parameters, ion‐neutral chemistry, and radiative processes in order to simulate some of the observable features regarding the temperatures, composition, and dynamical structure of the Venus atmosphere from 70 to 170 km. Atmospheric processes are included based upon formulations used in previous Venus GCMs, several augmentations exist, such as improved horizontal and vertical momentum equations and tracking exothermic chemistry. Explicitly solving the momentum equations allows for the exploration of its dynamical effects on the day‐night structure. In addition, V‐GITM's use of exothermic chemistry instead of a strong heating efficiency accounts for the heating due to the solar EUV while producing comparable temperatures to empirical models. V‐GITM neutral temperatures and neutral‐ion densities are compared to upper atmosphere measurements obtained from Pioneer Venus and Venus Express. V‐GITM demonstrates asymmetric horizontal wind velocities through the cloud tops to the middle thermosphere and explains the mechanisms for sustaining the wind structure. In addition, V‐GITM produces reasonable dayside ion densities and shows that the neutral winds can carry the ions to the nightside via an experiment advecting O2+ ${\mathrm{O}}_{2}^{+}$. Plain Language Summary: We present a new, state‐of‐the‐art Venus global circulation model, the Venus global ionosphere thermosphere model. The new model will be useful for answering unknown questions about Venus' atmosphere, the physics of CO2 rich planets, and Venus missions utilizing the aerobraking maneuver. We performed many model simulations and compared the results to some of the existing Venus data sets to assess the accuracy of the model. The results showed that the dayside extreme ultraviolet heating can be captured using the dominant ion‐neutral chemistry rather than relying on simplified legacy methods. Furthermore, the cloud layer below the thermosphere has a unique wind pattern and its impact on the thermospheric temperatures and winds were explored. This work also revealed that the thin nightside ionosphere forms as a result of the transport of a single ion species. Key Points: A new, non‐hydrostatic Venus ionosphere‐thermosphere model is introduced with new physics not previously included in Venus GCMsSimulations during solar minimum conditions are used for data‐model comparisons of the temperatures, plasma, and neutral densitiesThe influence of the retrograde superrotating zonal flow is explored in relation to how it affects the neutral temperature and velocities [ABSTRACT FROM AUTHOR]

This paper examines spectra of horizontal kinetic energy (HKE) in the surface and sub‐surface ocean, with an emphasis on internal gravity wave (IGW) motions, in global high‐resolution ocean simulations. Horizontal wavenumber‐frequency spectra of surface HKE are computed over seven oceanic regions from two global simulations of the HYbrid Coordinate Ocean Model (HYCOM) and three global simulations of the Massachusetts Institute of Technology general circulation model (MITgcm). In regions with high IGW activity, high surface HKE variance in the horizontal wavenumber‐frequency spectra is aligned along IGW linear dispersion curves. For both HYCOM and MITgcm, and in almost all regions, finer horizontal resolution yields more energetic supertidal IGW continuum spectra. The ratio of high‐horizontal‐wavenumber variance in semi‐diurnal and supertidal motions relative to lower‐frequency motions, a quantity of great interest for swath altimetry, depends on the model employed and the horizontal resolution within the model, implying that quantitative predictions of the partition between low‐ and high‐frequency motions taken from particular simulations should be treated with care. The frequency‐vertical wavenumber spectra, frequency spectra, and vertical wavenumber spectra from the models are compared to spectra computed from McLane profilers at nine locations. In general, MITgcm spectra match the McLane profiler spectra more closely at high frequencies (|ω| > 4.5 cpd). In both models, vertical wavenumber spectra roll off more steeply than observations at high vertical wavenumbers (m > 10−2 cpm). The vertical wavenumber spectra in such models is an important target for improvement, due to turbulence production and dissipation that takes place at high vertical wavenumbers. Plain Language Summary: Recently, a small but growing number of global ocean models have begun to employ simultaneous tidal and atmospheric forcing. At the same time, increasing supercomputer power has allowed for simulations of oceanic motions with increasing accuracy, increasing feature (spatial) resolution, and more frequent time slices. Global ocean models with fine grid spacing, and simultaneous tidal and atmospheric forcing, host a vigorous spectrum of high‐frequency waves that control mixing over most of the ocean water column, and are important for many operational oceanography challenges. As an example of the latter, high‐resolution global internal wave models have been used to study the relative partition of high‐frequency versus low‐frequency motions at the small horizontal scales that will be measured by the new Surface Water Ocean Topography mission. The partition described above depends on the model employed and the grid spacing employed within that model, meaning that conclusions about the partition are dependent on the model used to estimate it. Comparisons between the models and vertically profiling instruments indicate that resolving fine scale motions in the vertical direction, where ocean mixing takes place, is not yet handled well by the models. Modeling of fine‐vertical scale motions is therefore an important future research direction. Key Points: Vertical wavenumber spectra of internal gravity wave kinetic energy in two high‐resolution global models are compared to observed spectraModels under‐estimate motions at high vertical wavenumbers (small vertical scales), flagging this as a target for model improvementThe ratio of high‐ versus low‐frequency surface kinetic energy at small horizontal scales is dependent on the model and grid spacings employed [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

We develop a doubly periodic version of the Simple Convection‐Permitting E3SM Atmosphere Model (SCREAM) to provide an efficient configuration for this global convection permitting model (GCPM), akin to a single column model often found in conventional general circulation models. The design details are explained, in addition to the extensive case library associated with the doubly periodic SCREAM (DP‐SCREAM) configuration. We demonstrate that doubly periodic cloud resolving models are useful tools to explore the horizontal resolution sensitivity of GCPMs, in addition to replicating biases seen in the global models. Using DP‐SCREAM, we show that SCREAM exhibits behaviors of a scale aware model as it is able to naturally partition between sub‐grid scale (SGS) and resolved vertical transport across the gray zone of turbulence. We show that SCREAM is reasonably scale insensitive when run at resolutions from 1 to 5 km, but can exhibit sensitivity, particularly for the shallow convective regime, when run at resolutions approaching that of large eddy simulations. We conclude that SGS parameterization improvements are likely needed to reduce this scale sensitivity. Plain Language Summary: Advances in computational resources have allowed climate simulations to be performed with very high resolution, which provides higher quality results. However, these simulations require a lot of time and computer resources to perform, which makes these models difficult to use for the common scientist. In this paper we develop a high‐resolution configuration which focuses on a specific point on the globe, enabling it to run fast and to use minimal computational resources. This allows users and developers to gauge how the model may perform before doing a computationally intensive global simulation. We show that this faster configuration is a useful tool to replicate problems that are found in the global model and is a valuable way to assess sensitivities of the model, particularly pertaining to choices made in its resolution. Key Points: Doubly periodic configurations represent an efficient framework to supplement global cloud resolving modelsShallow convection is sensitive to horizontal resolution in Simple Convection‐Permitting E3SM Atmosphere Model (SCREAM) but stratiform clouds are more robustSCREAM exhibits the behaviors of a scale aware model and can represent a range of cloud regimes [ABSTRACT FROM AUTHOR]

The Indo-Pacific Ocean appears exponentially stratified between 1- and 3-km depth with a decay scale on the order of 1 km. In his celebrated paper "Abyssal recipes," W. Munk proposed a theoretical explanation of these observations by suggesting a pointwise buoyancy balance between the upwelling of cold water and the downward diffusion of heat. Assuming a constant upwelling velocity w and turbulent diffusivity κ, the model yields an exponential stratification whose decay scale is consistent with observations if κ ∼ 10−4 m2 s−1. Over time, much effort has been made to reconcile Munk's ideas with evidence of vertical variability in κ, but comparably little emphasis has been placed on the even stronger evidence that w decays toward the surface. In particular, the basin-averaged w nearly vanishes at 1-km depth in the Indo-Pacific. In light of this evidence, we consider a variable-coefficient, basin-averaged analog of Munk's budget, which we verify against a hierarchy of numerical models ranging from an idealized basin-and-channel configuration to a coarse global ocean simulation. Study of the budget reveals that the decay of basin-averaged w requires a concurrent decay in basin-averaged κ to produce an exponential-like stratification. As such, the frequently cited value of 10−4 m2 s−1 is representative only of the bottom of the middepths, whereas κ must be much smaller above. The decay of mixing in the vertical is as important to the stratification as its magnitude. Significance Statement: Using a combination of theory and numerical simulations, it is argued that the observed magnitude and shape of the global ocean stratification and overturning circulation appear to demand that turbulent mixing increases quasi-exponentially toward the ocean bottom. Climate models must therefore prescribe such a vertical profile of turbulent mixing in order to properly represent the heat and carbon uptake accomplished by the global overturning circulation on centennial and longer time scales. [ABSTRACT FROM AUTHOR]

The thermospheric neutral density response to the 7–9 September 2017 storms is investigated based on the Swarm satellite observations and the thermosphere‐ionosphere‐electrodynamic general circulation model (TIEGCM) simulation. The Swarm data depicted a prominent interhemispheric asymmetry (IHA) in the afternoon sector during the second storm, a feature that was yet explained. Driven by realistic high‐latitude electric potential and electron precipitation patterns, the TIEGCM is able to reproduce the observed storm‐time neutral density response. The TIEGCM simulation reveals that the differences in the traveling atmospheric disturbances (TADs) is largely responsible for the observed IHA in the neutral mass density response at low and middle latitudes, whereas the difference in mean molecular mass between the two hemispheres may contribute to the IHA in neutral density at higher latitudes. The IHAs in TADs and mean molecular mass are attributed to the IHA in Joule heating dissipation on the night and dawn sides. Plain Language Summary: During geomagnetic storms, thermospheric neutral density can increase drastically due to intense heat inputs. Enhanced neutral density increases the air drag on satellites which could lower their orbits and lead to faster atmosphere reentering of satellites. In addition, enhanced neutral mass density may cause larger uncertainties in orbit prediction which increases the risk of collisions with other space objects. Therefore, it is essential to improve the understanding of storm‐time neutral density variations to improve satellite operations. Recent satellite observations have revealed that thermospheric neutral mass density response can be interhemispherically asymmetric even under equinox conditions when the background density in the two hemispheres is comparable. This paper presents a case study of such an event based on data analysis and numerical simulations to investigate the physical mechanisms behind this intriguing phenomenon. The event took place on 7–9 September 2017 and the most prominent interhemispheric asymmetry of neutral density occurred in the afternoon sector. Our study found that the interhemispheric asymmetry during this event results from the differences in the generation, propagation and interaction of globally propagating atmospheric waves, namely traveling atmospheric disturbances, as well as the difference in mean molecular mass between the northern and southern hemispheres. Key Points: Thermosphere‐ionosphere‐electrodynamic general circulation model captures the observed interhemispheric asymmetry (IHA) in the neutral density response to the 7–9 September 2017 geomagnetic stormsThe IHA in the neutral density response results from the IHA in traveling atmospheric disturbances and mean molecular massThe observed IHA in the neutral density response is not caused by direct Joule heating dissipated at the same local time [ABSTRACT FROM AUTHOR]

When anticyclonic conditions persist over mountainous regions in winter, cold-air pools (i.e. thermal inversion layers) develop in valleys and persist from a few days to a few weeks. During these persistent cold-air pool episodes (PCAPs) the atmosphere inside the valley is stable and vertical mixing is prevented, promoting the accumulation of pollutants close to the valley bottom and worsening air quality. It has been shown from reanalysis that the Greater Alpine Region has warmed by three degrees over the last four decades. The purpose of this paper is to address the impact of climate change on PCAPs until the end of this century for the alpine Grenoble valleys. The long-term projections produced with the general circulation model MPI downscaled over the Alps with the regional climate model MAR (Modεave;le Atmosphérique Régional) are used to perform a statistical study of PCAPs over the 21st century. The trends of the main characteristics of PCAPs, namely their duration, frequency, and intensity, are investigated for two future scenarios, SSP2-4.5 and SSP5-8.5. We find that the intensity of PCAPs over the 21st century displays a statistically significant decreasing trend for the SSP5-8.5 scenario only, with a very weak decay rate of 0.058 K km−1 decade−1. The impact of climate change on the detailed structure of PCAPs is next investigated by comparing two such episodes, in the past and around 2050 considering the worst-case scenario (SSP5-8.5). For this purpose, the WRF (Weather Research and Forecasting) model, forced by MAR, is used at a high resolution (111 m). The episodes are carefully selected so that a meaningful comparison can be performed. We find that these episodes present similar atmospheric circulation and heat deficit across the valley depth but different atmospheric stability and (therefore) a different inversion height. The future episode is characterised by stronger atmospheric stability and a lower inversion height and about 4 degrees warmer air both close to the surface and in altitude. Overall, this study shows that the atmosphere in the Grenoble valleys tends to be slightly less stable in the future, under the SSP5-8.5 scenario, but that intense PCAPs can still form. [ABSTRACT FROM AUTHOR]

Precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily-precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a tenfold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of 'tropical cyclone worlds' to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly 'turning on' mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill. [ABSTRACT FROM AUTHOR]

The mixing of tracers by mesoscale eddies, parameterized in many ocean general circulation models (OGCMs) as a diffusive‐advective process, contributes significantly to the distribution of tracers in the ocean. In the ocean interior, diffusive contribution occurs mostly along the direction parallel to local neutral density surfaces. However, near the surface of the ocean, small‐scale turbulence and the presence of the boundary itself break this constraint and the mesoscale transport occurs mostly along a plane parallel to the ocean surface (horizontal). Although this process is easily represented in OGCMs with geopotential vertical coordinates, the representation is more challenging in OGCMs that use a general vertical coordinate, where surfaces can be tilted with respect to the horizontal. We propose a method for representing the diffusive horizontal mesoscale fluxes within the surface boundary layer of general vertical coordinate OGCMs. The method relies on regridding/remapping techniques to represent tracers in a geopotential grid. Horizontal fluxes are calculated on this grid and then remapped back to the native grid, where fluxes are applied. The algorithm is implemented in an ocean model and tested in idealized and realistic settings. Horizontal diffusion can account for up to 10% of the total northward heat transport in the Southern Ocean and Western boundary current regions of the Northern Hemisphere. It also reduces the vertical stratification of the upper ocean, which results in an overall deepening of the surface boundary layer depth. Finally, enabling horizontal diffusion leads to meaningful reductions in the near‐surface global bias of potential temperature and salinity. Plain Language Summary: Mesoscale ocean eddies, which are analogous to the weather systems in the atmosphere, are crucial to the distribution of heat, salt, carbon, and nutrients throughout the global ocean. Most of the ocean models used in climate simulations do not have enough horizontal resolution to resolve these eddies and, therefore, their effects must be parameterized. In the ocean interior, where no appreciable heat or mass is exchanged across density surfaces, the mixing of tracers due to mesoscale eddies occurs along surfaces of constant density. However, as ocean boundaries are approached, mixing occurs on a plane parallel to the boundary. For example, near the surface of the ocean, which is where the scheme presented here is designed to work, this plane is mostly horizontal. There is a class of ocean models that use a vertical coordinate system whose layer thicknesses can vary horizontally, thus complicating the implementation of horizontal diffusive parameterizations. This paper presents and evaluates a method that allows horizontal fluxes to be calculated and applied within the surface layer of this class of ocean models. Key Points: General vertical coordinate ocean models lack the option to shift diffusive fluxes from neutral surfaces to a purely horizontal directionRegridding/remapping techniques are used to represent tracers in z*‐coordinates, where horizontal fluxes are easily appliedThe method reduces global tracer biases in forced simulations, regardless of the coordinate system employed [ABSTRACT FROM AUTHOR]

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The ocean general circulation model (OGCM) is an essential tool for researching oceanography and atmospheric science. The LASG/IAP climate system ocean model version 3 (LICOM3) is a parallel version of the OGCM. Our goal is to implement and optimize a GPU version of LICOM3 based on compute unified device architecture (CUDA) called LICOM3-CUDA. Considering the characteristics of LICOM3 and CUDA, we design and implement some pivotal optimization methods, including redesigning the numerical algorithms of complicated functions, decoupling data dependency, avoiding memory write conflicts, and optimizing communication. In this paper, we selected two experiments, including 1 ∘ (small-scale) and 0.1 ∘ (large-scale) resolutions to evaluate the performance of LICOM3-CUDA. Under the experimental environment of two Intel Xeon Gold 6148 CPUs and four NVIDIA Quadro GV100s, the LICOM3-CUDA (1 ∘ ) achieves a simulation speed of 114.3 simulation-year-per-day (SYPD). Compare with the performance of LICOM3, the LICOM3-CUDA can run much faster with 6.5 times, and the compute-intensive module achieves over 70 × speedup. In addition, the energy consumption for the simulation year is reduced by 41.3%. As for high-resolution and large-scale simulation, the number of GPUs increased from 96 to 1536 as well as the LICOM3-CUDA (0.1 ∘ ) time consumption decreased from 3261 to 720 seconds with approximately 4.5 × of speedup. [ABSTRACT FROM AUTHOR]

Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NNs), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach, which is slower but more robust than full emulation, is to use NNs to predict optical properties but keep the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP (Rapid Radiative Transfer Model for General circulation model applications–Parallel) gas optics scheme and shown to be accurate while improving speed. However, the evaluations were based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using two new methods to improve accuracy – a hybrid loss function designed to reduce radiative forcing errors and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark – we train NN models on RRTMGP k distributions with reduced spectral resolutions. Offline evaluation of the new NN gas optics, RRTMGP-NN 2.0, shows a very high level of accuracy for clear-sky fluxes and heating rates. For instance, the RMSE in the shortwave surface downwelling flux is 0.78 W m -2 for RRTMGP and 0.80 W m -2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for five important greenhouse gases across a wide range of concentrations such as 8×CO2. To assess the impact of different gas optics schemes in the IFS, four 1-year coupled ocean–atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing schemes and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster) and is also faster than the older and less accurate RRTMG, which is used in the current operational cycle of the IFS. [ABSTRACT FROM AUTHOR]

Despite phytoplankton contributing roughly half of the photosynthesis on earth and fueling marine food‐webs, field measurements of phytoplankton biomass remain scarce. The particulate backscattering coefficient (bbp) has often been used as an optical proxy to estimate phytoplankton carbon biomass (Cphyto). However, total observed bbp is impacted by phytoplankton size, cell composition, and non‐algal particles. The lack of phytoplankton field data has prevented the quantification of uncertainties driven by these factors. Here, we first review and discuss existing bbp algorithms by applying them to bbp data from the BGC‐Argo array in surface waters (<10 m). We find a bbp threshold where estimated Cphyto differs by more than an order of magnitude. Next, we use a global ocean circulation model (the MITgcm Biogeochemical and Optical model) that simulates plankton dynamics and associated inherent optical properties to quantify and understand uncertainties from bbp‐based algorithms in surface waters. We do so by developing and calibrating an algorithm to the model. Simulated error‐estimations show that bbp‐based algorithms overestimate/underestimate Cphyto between 5% and 100% in surface waters, depending on the location and time. This is achieved in the ideal scenario where Cphyto and bbp are known precisely. This is not the case for algorithms derived from observations, where the largest source of uncertainty is the scarcity of phytoplankton biomass data and related methodological inconsistencies. If these other uncertainties are reduced, the model shows that bbp could be a relatively good proxy for phytoplankton carbon biomass, with errors close to 20% in most regions. Plain Language Summary: Phytoplankton contribute roughly half of the photosynthesis on earth and fuel fisheries around the globe. Yet, few direct measurements of phytoplankton concentration are available. Frequently, concentrations of phytoplankton are instead estimated using the optical properties of water. Backscattering is one of these optical properties, representing the light being scattered backwards. Previous studies have suggested that backscattering could be a good method to estimate phytoplankton concentration. However, other particles that are present in the ocean also contribute to backscattering. In this paper we examine how well backscattering can be used to estimate phytoplankton. To address this question, we use data from drifting instruments that are spread across the ocean and a computer model that simulates phytoplankton and backscattering over the global oceans. We find that by using backscattering, phytoplankton can be overestimated/underestimated on average by ∼20%. This error differs between regions, and can be larger than 100% at high latitudes. Computer simulations allowed us to quantify spatial and temporal variability in backscattering signal composition, and thereby understand potential errors in inferring phytoplankton with backscattering, which could not have been done before due to the lack of phytoplankton data. Key Points: Phytoplankton carbon bbp‐based algorithms can differ up to an order of magnitude at low bbp valuesAn algorithm fitted to a global model output shows biases ranging between 15% and 40% in most regionsMost uncertainties are due to the relative contribution of phytoplankton to total bbp [ABSTRACT FROM AUTHOR]

Radiative transfer models are widely applied in climate models to simulate vertical temperature perturbations caused by external radiative forcings. A large part of radiative transfer models is the infrared gaseous spectral transmittance scheme, which quantifies the longwave atmospheric absorption. A rapid infrared gaseous spectral transmittance scheme, called the Optimized alternate Mapping Correlated K‐Distribution model (OMCKD), is introduced in this paper. To improve the accuracy of our scheme without increasing pseudo‐monochromatic calculations, we introduce the optimal iteration method to automatically tune the equivalent absorption coefficients in the cumulative probability subspace. In addition, a new expression weighted by black‐body radiation is introduced to calculate the equivalent absorption coefficient. The OMCKD simulates heating rate and radiation flux with errors of less than 0.12 K d−1 and 0.35 W m−2, respectively, below stratopause for standard atmospheric profiles. The OMCKD is also evaluated and compared with the rapid radiative transfer model for general circulation models (RRTMG) in realistic atmospheric profiles. We found that OMCKD can accurately produce heating rates and generally captures radiative forcings associated with large perturbations to the concentrations of main greenhouse gases. Furthermore, the number of pseudo‐monochromatic calculations in OMCKD is 11.4% less than that in RRTMG, which indicates less computational cost. Plain Language Summary: Radiative models are practical tools for studying the greenhouse effect. The thermal infrared radiation absorbed by greenhouse gases is quantified by a specially designed program called the radiation scheme. The scheme divides the thermal infrared band into hundreds of intervals and calculates each interval's gaseous radiation absorption ability separately. Generally, increasing the number of intervals improves simulation accuracy, but the computational cost is proportional to the number of intervals. We have developed a rapid infrared gaseous spectral transmittance scheme called the Optimized alternate Mapping Correlated K‐Distribution model (OMCKD). Our scheme introduces an optimal method to adjust the radiation absorption ability of each interval with the line‐by‐line integration scheme (accurate but extremely time‐consuming) as the standard. We succeeded in improving the accuracy of our scheme without increasing the number of intervals. The OMCKD simulates heating rate and radiation flux with errors of less than 0.12 K d−1 and 0.35 W m−2, respectively, below stratopause for standard atmospheric conditions. The OMCKD generally captures the changes in the net fluxes and heating rates associated with perturbations to the concentrations of five main greenhouse gases. The stability of OMCKD implies its potential to improve the accuracy of climate predictions under higher CO2 concentration scenarios. Key Points: A rapid infrared gaseous spectral transmittance scheme based on the correlated K‐distribution method and the optimal method is developedThe optimal iteration method can significantly improve the accuracy of heating rates simulated with the correlated K‐distribution modelOur infrared gaseous spectral transmittance scheme maintains high accuracy across various carbon dioxide concentration scenarios [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

Accurate climate projections help policymakers mitigate the negative effects of climatic changes and prioritize environmental issues based on scientific evidences. These projections rely heavily on the outputs of GCMs (General Circulation Models), but the large number of GCMs and their different outputs in each region confuses researchers in their selection. In this paper, we analyzed the performance of a CMIP6 (Climate Model Intercomparison Project Phase 6) multi-model ensemble for Pr (precipitation) data over NE (Northern Europe). First of all, we evaluated the overall performance of 12 CMIP6 models from GCMs in 30 years of 1985–2014. Furthermore, future projections were analyzed between 2071 and 2100 using SSP1-2.6 and SSP5-8.5 (Shared Socioeconomic Pathways). Then, simulations were statistically improved using an ensemble method to correct the systematic error of the CMIP6 models and then the capacity of postprocessed data to reproduce historical trends of climate events was investigated. Finally, the possible spatio-temporal changes of future Pr data were explored in Tana River Basin. The results of this study show that different CMIP6 models do not have the same accuracy in estimating Pr in the study area. However, the ensemble method can be effective in increasing the accuracy of the projections. The results of this study projected a change in the monthly Pr data over Tana River Basin by 2.46% and 2.06% from 2071 to 2100 compared to the historical period, based on SSP1-2.6 and SSP5-8.5, respectively. [ABSTRACT FROM AUTHOR]

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

Dansgaard–Oeschger (D–O) events, millennial-scale climate oscillations between stadial and interstadial conditions (of up to 10–15 ∘ C in amplitude at high northern latitudes), occurred throughout the Marine Isotope Stage 3 (MIS3; 27.8–59.4 ka) period. The climate modelling community up to now has not been able to answer the question of whether our climate models are too stable to simulate D–O events. To address this, this paper lays the ground-work for a MIS3 D–O protocol for general circulation models which are used in the International Panel for Climate Change (IPCC) assessments. We review the following: D–O terminology, community progress on simulating D–O events in these IPCC-class models (processes and published examples), and evidence about the boundary conditions under which D–O events occur. We find that no model exhibits D–O-like behaviour under pre-industrial conditions. Some, but not all, models exhibit D–O-like oscillations under MIS3 and/or full glacial conditions. Greenhouse gases and ice sheet configurations are crucial. However most models have not run simulations of long enough duration to be sure which models show D–O-like behaviour, under either MIS3 or full glacial states. We propose a MIS3 baseline protocol at 34 ka, which features low obliquity values, medium to low MIS3 greenhouse gas values, and the intermediate ice sheet configuration, which our review suggests are most conducive to D–O-like behaviour in models. We also provide a protocol for a second freshwater (Heinrich-event-preconditioned) experiment, since previous work suggests that this variant may be helpful in preconditioning a state in models which is conducive to D–O events. This review provides modelling groups investigating MIS3 D–O oscillations with a common framework, which is aimed at (1) maximising the chance of the occurrence of D–O-like events in the simulations, (2) allowing more precise model–data evaluation, and (3) providing an adequate central point for modellers to explore model stability. [ABSTRACT FROM AUTHOR]

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

Finally, with respect to the inclusion of a contribution regarding the river hydrological system, Campuzano et al. [[11]] aim to explore the potential impacts on the ocean system that can be obtained by using sophisticated land boundary conditions based on the capacities of state-of-the-art hydrologic models combined with observation networks. Improving Operational Ocean Models for the Spanish Port Authorities: Assessment of the SAMOA Coastal Forecasting Service Upgrades. The paper assesses the potential impacts that newly available river discharge datasets can have on regional ocean model solutions delivered by the Copernicus Marine IBI-MFC regional service when used as part of the freshwater land forcing. [Extracted from the article]

This study further evaluates the modeling approach of Jia et al. (2019), https://doi.org/10.1029/2019gl081960 (JEA19) to investigate the potential effects of mesoscale sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a slab ocean model to produce two ensembles of simulations: one in which the SST exhibits realistic mesoscale variability, and another in which the mesoscale SST variability is suppressed. The results of the present study suggest that the modeling approach yields the desired SST differences between the two ensembles at the mesoscales. They also show, however, that the approach can lead to undesirable SST differences at the large scales, if the prescribed pair of oceanic heat flux fields have large scale differences. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by nonlinear interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat flux fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Plain Language Summary: This study evaluates a modeling approach to investigate the potential effects of mesoscale (scales smaller than 100 km) ocean sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a thermodynamical model of the ocean. Two ensembles of model simulations are prepared: one in which the ocean exhibits realistic mesoscale SST variability, and another in which that variability is suppressed. The results suggest that the modeling approach yields the desired mesoscale SST differences. They also show, however, that the approach can lead to undesirable large scale SST differences, if there are large scale differences between the prescribed oceanic heat transport fields of the two ensembles. In the experiments of JEA19, such forced, large scale SST differences dominate over the large scale differences that may develop in response to changes in the large scale atmospheric circulation by two‐way interactions with the SST. This result suggests that finding a proper pair of estimates of the oceanic heat transport fields is necessary for the investigation of the potential upscale impact of mesoscale SST variability on the atmosphere by the approach of JEA19. The paper concludes with proposing a potential improved strategy to obtain such a pair of estimates. Key Points: The large scale response in an uncoupled atmospheric simulation is the response of the model rather than that of the atmosphereA coupled modeling approach is necessary to investigate the large scale atmospheric response to mesoscale sea surface temperature variabilityA pair of oceanic heat fluxes that do not have large scale differences is critical to produce a realistic large scale atmospheric response [ABSTRACT FROM AUTHOR]

The prediction of internal solitary waves (ISWs) propagation is a difficult problem in the field of oceanography due to the complexity of its generative mechanism and the lack of in-situ data. In this paper, the simulation data is used to establish a prediction model of ISWs propagation in the southern Andaman Sea by deep learning. The global tidal model TPXO7.2 is used to obtain the semi-diurnal tidal velocity data at the generation source of ISWs. The generation and propagation characteristics of ISWs are obtained by the Massachusetts Institute of Technology General Circulation Model (MITgcm). The generative location, amplitude, and propagation characteristics of the ISWs are calculated based on MITgcm. The database is set up by the extracted data from MITgcm. Then, the prediction model of ISWs propagation is established using long and short-term memory (LSTM) networks. Finally, the accuracy of the model is verified by the ISWs Moderate Resolution Imaging Spectroradiometer (MODIS) images of the southern Andaman Sea and in-situ data. The results demonstrate that, compared with the MODIS images, the average relative error of time predicted by the LSTM model is 17.81%, and the average absolute error is 0.95 h. The average relative error of longitude predicted by the LSTM model is 0.10%, and the average absolute error is 0.10°. The average of the amplitudes predicted by the LSTM model is 56.99 m. The in-situ data of the amplitude of ISWs is 60 m in the previous research. The two amplitudes are relatively close. [ABSTRACT FROM AUTHOR]

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

General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented. [ABSTRACT FROM AUTHOR]

Uncertainty sources in climate change impact studies can be addressed in two distinct phases, climate modelling and impact assessment. Generally, uncertainty from the climate modelling phase is the larger contributor and the impact phase uncertainty is sometimes marginalized. This paper aims to develop integrated uncertainty analysis approaches that deal with uncertainties at the impact level with all sources included. The existing uncertainty analysis method of Reliability Ensemble Averaging (REA) is modified for impact studies by bracketing the hydrological model parameter uncertainty. A novel probabilistic approach referred to as Bootstrapped Ensemble Uncertainty Modelling (BEUM) is also proposed for uncertainty analysis at the impact phase. Modified REA and BEUM are applied to streamflow projections of the Hulu Langat basin with uncertainties stemming from emission scenarios, general circulation models (GCM), downscaling method, and a hydrological model parametrization for a comprehensive integrated uncertainty assessment. The modified REA increased the reliability measure of each streamflow scenario and improved the uncertainty coverage percentage when compared with the original REA. The REA eliminated the projections of the less reliable GCMs, and therefore reduced uncertainty bands compared to the BEUM. The BEUM can create a large ensemble of streamflow projections through nonparametric bootstrapping of the limited impact scenarios and define the streamflow distribution for every quantile. Therefore, BEUM is able to encompass larger uncertainty intervals and explain the behavior of ensemble members through probabilistic distributions that can assist in decision making for climate change mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

Parameterized boundary layer turbulence and moist convection remain some of the largest sources of uncertainty in general circulation models. High-resolution climate modeling aims to reduce that uncertainty by explicitly attempting to resolve deep moist convective motions. An example of such a model is the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) with a target global resolution of 3.25 km, allowing for a more accurate representation of complex mesoscale deep convective dynamics. Yet, small-scale planetary boundary layer turbulence and shallow convection still need to be parameterized, which in SCREAM is accomplished through the turbulent-kinetic-energy-based (TKE-based) simplified higher-order closure (SHOC) – a simplified version of the assumed-double-Gaussian-PDF (probability density function) higher-order-closure method. In this paper, we implement a stochastic-multiplume mass-flux (MF) parameterization of dry and shallow convection in SCREAM to go beyond the limitations of double-Gaussian-PDF closures and couple it to SHOC (SHOC + MF). The new parameterization implemented in a single-column model type version of SCREAM produces results for two shallow cumulus convection cases (marine and continental shallow convection) that agree well with the reference data from large-eddy simulations, thus improving the general representation of the thermodynamic quantities and their turbulent fluxes as well as cloud macrophysics in the model. Furthermore, SHOC + MF parameterization shows weak sensitivity to the vertical grid resolution and model time step. [ABSTRACT FROM AUTHOR]

Using the 5 km coupled general circulation model ICON, the surface internal wave energy source, crucial for the oceanic circulation, is quantified as the wind‐induced wave energy flux that radiates from the mixed layer bottom (MLB) into the ocean interior. Our result lowers the previous estimates of the wind power input to surface near‐inertial motions from up to more than 1 TW down to about 0.23–0.27 TW, depending on season. We point out that the estimate of the wind input to ocean depends not only on the wind stress used—as suggested by previous studies—but also on the ocean model used. While the surface currents in a slab ocean model or a non‐eddying ocean circulation model are strongly determined by the wind forcing, the surface currents in the 5 km ICON model can be more strongly determined by internal instability process (eddy) than by wind stress forcing from less‐extreme weather disturbances. The resulting more or less random alignment of surface current and wind stress can presumably lead to a lower wind input to surface near‐inertial motions. Of the surface wave energy source, about 30% is fluxed down into the interior ocean. This percentage roughly doubles those from previous studies, due to the stronger wave energy flux related to stronger inertial waves generated by the tropical cyclones simulated by the 5 km ICON model. Overall, the low wind input at near‐inertial frequencies produces a wind‐induced wave energy source at the MLB that is well below 0.1 TW. Plain Language Summary: For maintaining the oceanic overturning circulation, energy is needed to mix the dense water up and light water down. The main energy source for mixing arises from breaking of internal waves. A considerable portion of this source comes from waves excited by winds at the sea surface. This paper quantifies this wave energy source based on a frontier simulation of a coupled atmosphere‐ocean general circulation model at a horizontal resolution of 5 km. This model is capable to simulate tropical cyclones (hurricanes and typhoons) and less‐extreme small‐scale and short‐living weather disturbances and oceanic mesoscale eddies, which were not represented by the models used in most of the previous studies. Taking these new features into account, we find that the wind‐induced wave energy source is less than 0.1 TW. Key Points: Relatively low wind power input to near‐inertial motions in a 5 km global coupled simulationEnergy flux radiating from the mixed layer bottom as interior wave energy sourceStrong internal waves excited by tropical cyclones simulated by a 5 km global coupled general circulation model [ABSTRACT FROM AUTHOR]

Application of seasonal forecasts in agriculture has significant potential and realized utility. Other sectors that may also benefit from using seasonal forecasts include (but are not limited to) health, hydrology, water, and energy. This paper shows that seasonal forecast model data, satellite Pour l'Observation de la Terre (SPOT), dry matter productivity (DMP) data (proxy of grass biomass) along with other sets of data are effectively used to estimate grazing capacity (GC) over a 12-year test period (1998/1999–2009/2010) in Limpopo Province. GC comprises a vital consideration in agricultural activities, particularly for a province in South Africa like Limpopo, due to its varying climate. The Limpopo Province capitalizes on subsistence farming, including livestock and crop production. Grazing should thus be regulated in order to conserve grass, shrubs, and trees, thereby ensuring sustainability of rangelands. In a statistical downscaling model, the predictor is the 850 geopotential height fields of a coupled ocean–atmosphere general circulation (CGCM) over Southern Africa to predict seasonal DMP values. This model shows that the mid-summer rainfall totals are important predictors for the November through April (NDJFMA) DMP (as well as grazing capacity) growing season. Forecast verification is conducted using the relative operating characteristics (ROC) and reliability diagrams. The CGCM model shows skill in discriminating high and low DMP (GC) seasons in the Limpopo Province, as well as reliability in the probabilistic forecasts. This paper demonstrates the development of a tailored forecast, an avenue that should be explored in enhancing relevance of forecasts in agricultural production. [ABSTRACT FROM AUTHOR]

Statistical downscaling of General Circulation Models (GCM) simulations is widely used for projecting precipitation at different spatiotemporal scales. However, the downscaling process is linked with different source of uncertainty including structural/parametric uncertainty of the model and output uncertainty. This research proposes a novel framework to assess the parametric uncertainty of downscaling model, and used this framework to assess the performance of different bias correction methods linked to the regression-based statistical downscaling model. The used downscaling framework in the current paper is Statistical Downscaling Model (SDSM). The conventional bias correction method linked with SDSM is the Variance InFlation method (VIF), this paper substitutes this method with three different bias correction methods including Local Intensity Scaling (LOCI), Power Transformation (PT), and Quantile Mapping (QM) to assess the associated parametric and global uncertainty of each method in different climate by using a new approach. The proposed method is applied to six different stations located in Iran and United States with different climate status. Average Relative Interval Length (ARIL), P-level, and Normalized Uncertainty Efficiency (NUE) are used as uncertainty indicators to evaluate the results. Results represent that in every assessed climate class, LOCI, and PT, work better than conventional VIF in both amount and occurrence modules of SDSM framework. More precisely, LOCI works better in station that has wet summer, while PT performs well in the stations where there is no or very limited precipitation in summer. Substituting LOCI with VIF, result in increasing the value of NUE by at least factor of 3 in occurrence and amount model which means the significant reduction in structural uncertainty. Also applying PT in arid regions improves the NUE indicator at least by factor 2 in occurrence and amount model and by factor 3 in output uncertainty assessment, and results in less parametric and output uncertainty. Results illustrate the important role of bias correction approaches in reducing structural, and output uncertainty and improving the statistical efficiency of the downscaling model. [ABSTRACT FROM AUTHOR]