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Among the most critical issues, climatic abnormalities caused by global warming also affect Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary Typhoon Morakot hit Southern Taiwan on 8 August 2009 and induced serious flooding and landslides. In this study, the Kao-Ping River watershed was adopted as the study area, and the typical events 2007 Krosa Typhoon and 2009 Morakot Typhoon were adopted to train the susceptibility model. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the Kao-Ping River watershed. The rainfall estimates were introduced in the landslide susceptibility model to produce the predictive landslide susceptibility for various rainfall scenarios, including abnormal climate conditions. These results can be used for hazard remediation, miti gation, and prevention plans for the Kao-Ping River watershed. [ABSTRACT FROM AUTHOR]

Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multimodel predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño Southern Oscillation. [ABSTRACT FROM AUTHOR]

I/O is often a performance bottleneck in global ocean circulation models with fine spatial resolution. In this paper, we present an asynchronous parallel I/O framework and demonstrate its efficacy in the Mass Conservation Ocean Model (MaCOM) as a case study. By largely reducing I/O operations in computing processes and overlapping output in I/O processes with computation in computing processes, this framework significantly improves the performance of the MaCOM. Through both reordering output data for maintaining data continuity and combining file access for reducing file operations, the I/O optimizing algorithms are provided to improve output bandwidth. In the case study of the MaCOM, the cost of output in I/O processes can be overlapped by up to 99% with computation in computing processes as decreasing output frequency. The 1D data output bandwidth with these I/O optimizing algorithms is 3.1 times faster than before optimization at 16 I/O worker processes. Compared to the synchronous parallel I/O framework, the overall performance of MaCOM is improved by 38.8% at 1024 computing processes for a 7-day global ocean forecast with 1 output every 2 h through the asynchronous parallel I/O framework presented in this paper. [ABSTRACT FROM AUTHOR]

Many atmospheric global circulation models have large biases in predicting meridional and vertical winds and fluxes of gas species in remote regions such as the middle and upper atmosphere. In this study, we make sensitivity simulations to recognize the role of vital processes associated with dynamical coupling between different atmospheric layers, namely dynamical and thermal impacts of mesoscale orographic gravity waves (OGWs) generated by the Earth's topography and changes from the easterly to westerly QBO phases in the lower equatorial atmosphere. We improved parameterizations of OGW dynamical and thermal effects and QBO flows and implemented them into a general circulation model of the middle and upper atmosphere used in different countries. With this model, we study the sensitivity of meridional circulation and vertical velocity to stationary OGWs and to changes in QBO phases at altitudes up to 100 km in January. We also considered respective changes in vertical ozone fluxes in the atmosphere. Accounting stationary OGW effects gives changes up to 40% in the meridional velocity and associated ozone fluxes in the stratosphere. Transitions from the easterly to westerly QBO phase in tropics may significantly alter the meridional and vertical circulation of the middle atmosphere at middle and high latitudes: up to 60% from the peak respective values. The improved parameterizations of OGW and QBO effects have impacts on other features of the general circulation model, improving the simulation of general circulation, planetary and tidal wave coupling in the lower, middle and upper atmosphere. [ABSTRACT FROM AUTHOR]

Numerical simulations provide a considerable aid in studying past climates. Out of the various approaches taken in designing numerical climate experiments, transient simulations have been found to be the most optimal when it comes to comparison with proxy data. However, multi-millennial or longer simulations using fully coupled general circulation models are computationally very expensive such that acceleration techniques are frequently applied. In this study, we compare the results from transient simulations of the present and the last interglacial with and without acceleration of the orbital forcing, using the comprehensive coupled climate model CCSM3 (Community Climate System Model 3). Our study shows that in most parts of the world, the simulation of long-term variations in interglacial surface climate is not significantly affected by the use of the acceleration technique (with an acceleration factor 10) and, hence, large-scale model-data comparison of surface variables is not hampered. However, in high-latitude regions where the surface climate has a direct connection to the deep ocean, e.g. in the Southern Ocean or the Nordic Seas, acceleration-induced biases in sea-surface temperature evolution may occur with potential influence on the dynamics of the overlying atmosphere. [ABSTRACT FROM AUTHOR]

The STatistical Analogue Resampling Scheme (STARS) statistical approach was recently used to project changes of climate variables in Germany corresponding to a supposed degree of warming. We show by theoretical and empirical analysis that STARS simply transforms interannual gradients between warmer and cooler seasons into climate trends. According to STARS projections, summers in Germany will inevitably become dryer and winters wetter under global warming. Due to the dominance of negative interannual correlations between precipitation and temperature during the year, STARS has a tendency to generate a net annual decrease in precipitation under mean German conditions. Furthermore, according to STARS, the annual level of global radiation would increase in Germany. STARS can be still used, e.g., for generating scenarios in vulnerability and uncertainty studies. However, it is not suitable as a climate downscaling tool to access risks following from changing climate for a finer than general circulation model (GCM) spatial scale. [ABSTRACT FROM AUTHOR]

The Ent Terrestrial Biosphere Model (Ent TBM) is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models (GCMs). This study describes the leaf phenology submodel implemented in the Ent TBM version 1.0.1.0.0, coupled to the carbon allocation scheme of the Ecosystem Demography (ED) model. The phenology submodel adopts a combination of responses to temperature (growing degree days and frost-hardening), soil moisture (linearity of stress with relative saturation), and radiation (light length). Growth of leaves, sapwood, fine roots, stem wood, and coarse roots is updated on a daily basis. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites, with both observed and prognostic hydrology, and observed and prognostic seasonal leaf area index. The phenology submodel is able to capture the timing and magnitude of leafout and senescence for temperate broadleaf deciduous forest (Harvard Forest and Morgan-Monroe State Forest, US), C3 annual grassland (Vaira Ranch, US), and California oak savanna (Tonzi Ranch, US). For evergreen needleleaf forest (Hyytiäla, Finland), the phenology submodel captures the effect of frost-hardening of photosynthetic capacity on seasonal fluxes and leaf area. We address the importance of customizing parameter sets of vegetation soil moisture stress response to the particular land surface hydrology scheme. We identify model deficiencies that reveal important dynamics and parameter needs. [ABSTRACT FROM AUTHOR]

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

In this article we propose two conformal mapping based grid generation algorithms for global ocean general circulation models (OGCMs). Contrary to conventional, analytical forms based dipolar or tripolar grids, the new algorithms are based on Schwarz-Christoffel (SC) conformal mapping with prescribed boundary information. While dealing with the basic grid design problem of pole relocation, these new algorithms also address more advanced issues such as smoothed scaling factor, or the new requirements on OGCM grids arisen from the recent trend of high-resolution and multi-scale modeling. The proposed grid generation algorithm could potentially achieve the alignment of grid lines to coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the generated grids are still orthogonal curvilinear, they can be readily utilized in existing Bryan-Cox-Semtner type ocean models. The proposed methodology can also be applied to the grid generation task for regional ocean modeling where complex land-ocean distribution is present. [ABSTRACT FROM AUTHOR]

This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The conventional EAKF adopts linear and Gaussian assumptions, making it difficult to handle cross-component updates in strongly coupled data assimilation (SCDA). The new approach employs nonlinear variable relationships established by a deep neural network (DNN) during the analysis stage of the EAKF, which nonlinearly projects observation increments into the state variable space. It can diminish errors in estimating cross-component error covariance arising from insufficient ensemble members, therefore improving the SCDA analysis. A conceptual coupled model is employed in this paper to conduct twin experiments, validating the DNN–EAKF's capability to outperform conventional EAKF in SCDA. The results reveal that the DNN–EAKF can make SCDA superior to WCDA with a limited ensemble size. The root-mean-squared errors are reduced up to 70% while the anomaly correlation coefficients are increased up to 20% when the atmospheric observations are used to update the ocean variables directly. The other model components can also be improved through SCDA. This approach is anticipated to offer insights for future methodological integrations of machine learning and data assimilation and provide methods for SCDA applications in coupled general circulation models. [ABSTRACT FROM AUTHOR]

In this study we present first results of a new isotope-enabled general circulation model setup. The model consists of a fully coupled atmosphere-ocean model ECHAM5/MPIOM, enhanced by the interactive land surface scheme JSBACH and an explicit hydrological discharge scheme to close the global water budget. Stable water isotopes H182O and HDO have been incorporated into all relevant model components. Results of two equilibrium simulations under pre-industrial and last glacial maximum conditions are analysed and compared to observational data and paleoclimate records for evaluating the model's performance of simulating spatial and temporal variations in the isotopic composition of the Earth's water cycle. For the pre-industrial climate, many aspects of the simulation results of meteoric waters are in good to very good agreement with both observations and earlier atmosphere-only simulations. The model is capable of adequately simulating the large spread in the isotopic composition of precipitation between low and high latitudes. A comparison to available ocean data also shows a good model-data agreement, however a strong bias of too depleted ocean surface waters is detected for the Arctic region. Simulation results under last glacial maximum boundary conditions also fit to the wealth of available isotope records from polar ice cores, speleothems, as well as marine calcite data. Data-model evaluation of the isotopic composition in precipitation reveals a good match of the model results and indicates that the temporal glacial-interglacial isotope-temperature relation was substantially lower than the present spatial gradient for most mid- to high-latitudinal regions. As compared to older atmosphere-only simulations, a remarkable improvement is achieved for the modelling of the deuterium excess signal in Antarctic ice cores. Our simulation results indicate that cool sub-tropical and mid-latitudinal sea surface 25 temperatures are key for this progress. A recently discussed revised interpretation of the deuterium excess record of Antarctic ice cores in terms of marine relative humidity changes on glacial-interglacial timescales is not supported by our model results. [ABSTRACT FROM AUTHOR]

Seasonal and interannual variability in sinking flux of biogenic particles was reported by the multi-year bottom-tethered sediment trap measurements in the Northwind Abyssal Plain (Station NAP: 75°N, 162°W, 1975m water depth) of the western Arctic Chukchi Borderland. Whereas the trapped particle flux had an obvious peak with the dominance of sea ice-related diatom valve in August 2011, the observed particle flux was considerably suppressed throughout the summer season in 2012. In the present study, response of ice algal production and biomass to wind-driven changes in physical environments was addressed using a pan-Arctic sea ice-ocean modeling approach. Sea ice ecosystem with ice algae was newly incorporated into the lower-trophic marine ecosystem model, which was previously coupled with a high-resolution (i.e., horizontal grid size of 5 km) ocean general circulation model. Seasonal experiments covering two year-long mooring periods indicated that primary productivity of ice algae around the Chukchi Borderland depended on basin-scale wind pattern through various processes. Easterly wind in the southern part of distinct Beaufort High supplied high abundance of nutrient for euphotic zones of the NAP region via both surface Ekman transport of Chukchi shelf water and vertical turbulent mixing with underlying nutricline water as in 2011. In contrast, northwesterly wind flowing in the northern part of extended Siberian High transported oligotrophic water within the Beaufort Gyre circulation toward the NAP region as in 2012. The modeled ice algal biomass during the summer season certainly reflected the differences in nutrient distribution. The sinking flux of Particulate Organic Nitrogen (PON) was comparable with the time series obtained from the sediment trap data in summer 2011. On the other hand, lateral advection of shelf-origin ice algal patch during a great cyclone event might have caused a model bias on the PON flux in 2012. The extension of year-long measurements is expected to help the illustration of more general features on the Arctic marine biological pump. [ABSTRACT FROM AUTHOR]

In the North West Shelf region of Australia is a surface current (Holloway Current), which flows southwestward along the shelf break. This paper describes a seasonal undercurrent below the Holloway Current. A 5-day climatology is constructed from the output of an eddy-resolving oceanic general circulation model (OGCM). A seasonal northeastward-flowing undercurrent is found on the upper continental slope during the climatological April-May. This undercurrent reverses during February-March. During its annual cycle, the phase of the undercurrent tends to propagate southwestward and upward. The annual frequency dominates, but the positive and negative phases of the undercurrent are not symmetric in the yearly cycle because of the contributions from the semi-annual and 1/3-annual components. We propose a hypothesis that this undercurrent is a beam of coastal trapped wave (CTW). As an initial attempt to assess the plausibility of this hypothesis, we construct an idealized linear coastal-trapped wave (CTW) solution driven by an idealized harmonic meridional winds at the annual frequency. The solution takes the form of a beam originating from the forcing region on the continental shelf and propagating offshore and southward. When it emerges on to the continental slope, it takes the form of an undercurrent. This idealized solution shares several properties with the undercurrent in the OGCM despite several discrepancies. [ABSTRACT FROM AUTHOR]

There is a growing need for proactive planning for natural hazards in a changing climate. Computational modeling of climate hazards provides an opportunity to inform planning, particularly in areas approaching ecosystem state changes, such as Interior Alaska, where future hazards are expected to differ significantly from historical events in frequency and severity. This paper considers improved modeling approaches from a physical process perspective and contextualizes the results within the complexities and limitations of hazard planning efforts and management concerns. Therefore, the aim is not only to improve the understanding of potential climate impacts on streamflow within this region but also to further explore the steps needed to evaluate local-scale hazards from global drivers and the potential challenges that may be present. This study used dynamically downscaled climate forcing data from ERA-Interim reanalysis datasets and projected climate scenarios from two General Circulation Models under a single Representative Concentration Pathway (RCP 8.5) to simulate an observational gage-calibrated WRF-Hydro model to assess shifts in streamflow and flooding potential in three Interior Alaska rivers over a historical period (2008–2017) and two future periods (2038–2047 and 2068–2077). Outputs were assessed for seasonality, streamflow, extreme events, and the comparison between existing flood control infrastructure in the region. The results indicate that streamflow in this region is likely to experience increases in seasonal length and baseflow, while the potential for extreme events and variable short-term streamflow behavior is likely to see greater uncertainty, based on the divergence between the models. [ABSTRACT FROM AUTHOR]

The 1960s was an exciting era for atmospheric predictability research: a finite predictability of the atmosphere was uncovered using Lorenz's models and the well-acknowledged predictability limit of two weeks was estimated using a general circulation model (GCM). Here, we delve into details regarding how a correlation between the two-week predictability limit and a doubling time of five days was established, recognize Lorenz's pioneering work, and suggest non-impossibility for predictability beyond two weeks. We reevaluate the outcomes of three different approaches—dynamical, empirical, and dynamical-empirical—presented in Lorenz's and Charney et al.'s papers from the 1960s. Using the intrinsic characteristics of the irregular solutions found in Lorenz's studies and the dynamical approach, a doubling time of five days was estimated using the Mintz–Arakawa model and extrapolated to propose a predictability limit of approximately two weeks. This limit is now termed "Predictability Limit Hypothesis", drawing a parallel to Moore's Law, to recognize the combined direct and indirect influences of Lorenz, Mintz, and Arakawa under Charney's leadership. The concept serves as a bridge between the hypothetical predictability limit and practical model capabilities, suggesting that long-range simulations are not entirely constrained by the two-week predictability hypothesis. These clarifications provide further support to the exploration of extended-range predictions using both partial differential equation (PDE)-physics-based and Artificial Intelligence (AI)—powered approaches. [ABSTRACT FROM AUTHOR]

MATS (Mesospheric Airglow/Aerosol Tomography and Spectroscopy) is a Swedish satellite designed to investigate atmospheric dynamics in the mesosphere and lower thermosphere (MLT). By observing structures in noctilucent clouds over polar regions and oxygen atmospheric-band (A-band) emissions globally, MATS will provide the research community with properties of the MLT atmospheric wave field. Individual A-band images taken by MATS's main instrument, a six-channel limb imager, are transformed through tomography and spectroscopy into three-dimensional temperature fields, within which the wave structures are embedded. To identify wave properties, particularly the gravity wave momentum flux, from the temperature field, smaller-scale perturbations (associated with the targeted waves) must be separated from large-scale background variations using a method of scale separation. This paper investigates the possibilities of employing a simple method based on smoothing polynomials to separate the smaller and larger scales. Using using synthetic tomography data based on the HIAMCM (HIgh Altitude Mechanistic general Circulation Model), we demonstrate that smoothing polynomials can be applied to MLT temperatures to obtain fields corresponding to global-scale separation at zonal wavenumber 18. The simplicity of the method makes it a promising candidate for studying wave dynamics in MATS temperature fields. [ABSTRACT FROM AUTHOR]

All climate models use parameterizations and tuning in order to be accurate. The different parameterizations and tuning processes are the primary source of difference between models. Because models are tuned with present observations of Earth, they may not accurately simulate climates of other planets or palaeoclimate. A model with no adjustable parameter that happens to fit today's observations is probably more universal and should be more appropriate to model palaeoclimate. However, to our knowledge, such a model does not exist or is yet to be developed. This paper aims to improve a parameter-free radiative–convective model that computes a realistic temperature vertical profile to compute the water cycle, giving a value on average tropical precipitation. Although it is known that the radiative transfer constrains the order of magnitude of precipitation, no parameter-free model has yet been able to compute precipitation. Our model finds a precipitation value closer to observations than similar radiative–convective models or some general circulation models (GCMs). [ABSTRACT FROM AUTHOR]

Understanding historical soil moisture (SM) variations and their relationship with temperature in high‐latitude humid regions is essential for predicting hot droughts under widespread warming. This paper presents the first‐ever annual‐resolution summer surface SM reconstruction (1736–2006 CE) in Sweden, located in northern Europe (NE). The reconstruction utilizes the paleoclimate proxy, tree‐ring δ18O, which exhibits a strong correlation with reanalysis SM data during 1948–2007 CE (r = −0.67, p < 0.001). In contrast to an unprecedented trend suggested by previous studies, our reconstruction exhibits a wetting trend in NE since the 1960s falling within the historical spectrum of trends. Both reanalysis SM data and our reconstruction show significant negative correlations with temperature at interannual timescales. However, Coupled Model Intercomparison Project Phase 6 climate models failed to capture the significant SM‐temperature relationship during the historical (1850–1947 CE) and observation periods (1948–2006 CE). This proxy‐model comparison represents a critical step toward enhancing our understanding of SM dynamics and the reliability of SM projections in high‐latitude humid regions. Plain Language Summary: Soil moisture (SM) refers to water in any form contained in soils, and it plays an important role in sustaining plant growth and influencing climate dynamics. SM deficits can result in ecological/agricultural droughts. However, our understanding of historical SM changes is limited due to the scarcity of long‐term records. Consequently, modeling historical SM changes proves challenging and prone to inaccuracies. In this study, we utilized a tree‐ring record to reconstruct centuries‐long SM changes in Sweden, located in northern Europe. Our analysis revealed a significant negative correlation between SM and temperature in both our reconstruction and SM data sets. However, state‐of‐art models failed to capture the significant relationship observed at our study site. These findings highlight the need to improve climate models to increase the accuracy of climate forecasts and better understand SM dynamics in high‐latitude humid areas. Key Points: This study presents the first 271‐year historical soil moisture (SM) reconstruction in northern Europe (NE) using the tree‐ring δ18O proxySignificant correlations between historical SM and temperature in NE were observed at interannual timescalesOur results implied that Coupled Model Intercomparison Project Phase 6 models might underestimate the SM‐temperature relationship [ABSTRACT FROM AUTHOR]

The development and verification of the convective module of IL-GLOBO, a Lagrangian transport model coupled online with the Eulerian general circulation model GLOBO, is described. The online-coupling promotes the full consistency between the Eulerian and the Lagrangian components of the model. The Lagrangian convective scheme is derived based on the Kain-Fritsch convective parameterisation used in GLOBO. A transition probability matrix is computed using the fluxes provided by the Eulerian KF parameterisation. Then, the convection redistribution of Lagrangian particles is implemented via a Monte Carlo scheme. The formal derivation is described in details and, consistently with the Eulerian module, includes the environmental flux in the transition probability matrix to avoid splitting of the convection and subsidence processes. Consistency of the Lagrangian implementation with its Eulerian counterpart is verified by computing environment fluxes from the transition probability matrix and comparing them to those computed by the Eulerian module. Assessment of the impact of the module is made for different latitudinal belts, showing that the major impact is found in the tropics, as expected. Concerning vertical distribution, the major impact is observed in the boundary layer at every latitude, while in the tropical area, the influence extends to very high levels. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic approach for the aggregation process rate in the ICOsahedral Nonhydrostatic general circulation model (ICON-AES), which takes subgrid-scale variability into account. This method creates a stochastic parameterization of the process rate by choosing a new specific cloud ice mass at random from a uniform distribution function. This distribution, which is consistent with the model's cloud cover scheme, is evaluated in terms of cloud ice mass variance with a combined satellite retrieval product (DARDAR) from the satellite cloud radar CloudSat and the Cloud–Aerosol Lidar and Infrared Pathfinder Observations (CALIPSO). The global patterns of simulated and observed cloud ice mixing ratio variance are in a good agreement, despite an underestimation in the tropical regions, especially at lower altitudes, and an overestimation in higher latitudes from the modeled variance. Due to this stochastic approach the yearly mean of cloud ice shows an overall decrease. As a result of the nonlinear nature of the aggregation process, the yearly mean of the process rates increases when taking subgrid-scale variability into account. An increased process rate leads to a stronger transformation of cloud ice into snow and therefore to a cloud ice loss. The yearly averaged global mean aggregation rate is more than 20 % higher at selected pressure levels due to the stochastic approach. A strong interaction of aggregation and accretion, however, lowers the effect of cloud ice loss due to a higher aggregation rate. The new stochastic method presented lowers the bias of the aggregation rate. [ABSTRACT FROM AUTHOR]

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

In this paper we describe the implementation of the carbon isotopes 13 C and 14 C (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 (δ13 C DIC and Δ14 C DIC), 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 14 C, 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 Δ14 C DIC. [ABSTRACT FROM AUTHOR]

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China's largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people's daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new high-resolution (1/10 °) data-driven dataset of 3D ocean currents developed by the National Research Council of Italy in the framework of the European Space Agency World Ocean Circulation project: the WOC-NATL3D dataset. The product domain extends over a wide portion of the North Atlantic Ocean from the surface down to 1500 m depth, and the dataset covers the period between 2010 and 2019. To generate this product, a diabatic quasi-geostrophic diagnostic model is applied to data-driven 3D temperature and salinity fields obtained through a deep learning technique, along with ERA5 fluxes and empirical estimates of the horizontal Ekman currents based on input provided by the European Copernicus Marine Service. The assessment of WOC-NATL3D currents is performed by direct validation of the total horizontal velocities with independent drifter estimates at various depths (0, 15 and 1000 m) and by comparing them with existing reanalyses that are obtained through the assimilation of observations into ocean general circulation numerical models. Our estimates of the ageostrophic components of the flow improve the total horizontal velocity reconstruction, being more accurate and closer to observations than model reanalyses in the upper layers, also providing an indirect proof of the reliability of the resulting vertical velocities. The reconstructed WOC-NATL3D currents are freely available at 10.12770/0aa7daac-43e6-42f3-9f95-ef7da46bc702. [ABSTRACT FROM AUTHOR]

The performance of general circulation models (GCMs) in simulating horizontal winds is important because the distribution and variation in horizontal winds are central to investigating atmospheric dynamic characteristics and processes. Also, horizontal wind data can be used to extract some of the required information on gravity waves, tides, and planetary waves. In this context, the present paper evaluates the capability of the Institute of Atmospheric Physics atmospheric general circulation model high-top version (IAP-HAGCM) in simulating the horizontal winds and tides of the troposphere and lower stratosphere by presenting a climatological and statistical comparison against observations of the powerful Beijing mesosphere–stratosphere–troposphere (MST) radar (39.78°N, 116.95°E) during 2012–2014. The results illustrated that the IAP-HAGCM can successfully reproduce the time–altitude distribution of the monthly mean zonal wind and diurnal tide amplitude, albeit with some underestimation. The mean correlation coefficients and root-mean-square error for the zonal (meridional) winds were 0.94 (0.73) and 6.60 m s−1 (2.90 m s–1), respectively. Additionally, the IAP-HAGCM can capture the temporal variation in both the zonal and meridional winds. It is worth noting that, compared with the seven coupled model intercomparison project phase 6 (CMIP6) models, the IAP-HAGCM performs better in meridional wind simulations below 15 km. However, there are discrepancies in altitudinal ranges with large wind velocities, such as the westerly jet, in the transition region of the troposphere and stratosphere, and in February, April, July, and September. It is suggested that model users should take advantage of the model's simulation ability by combining this information regarding when and where it is optimal with their own research purposes. Moreover, the evaluation results in this paper can also serve as a reference for guiding improvements of the IAP-HAGCM. [ABSTRACT FROM AUTHOR]

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO 2 emissions and non-CO 2 impacts, such as NO x emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NO x emitted, or per flown kilometre. In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NO x -induced ozone and H 2 O effects are well represented by the ACCF model output. The NO x -induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NO x -induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NO x -induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO 2 contribution observed in this study is low compared with the non-CO 2 effects, which requires further diagnosis. [ABSTRACT FROM AUTHOR]

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

There is great uncertainty about the future effects of climate change on the global economic, social, environmental, and water sectors. This paper focuses on watershed vulnerabilities to climate change by coupling a distributed hydrological model with artificial neural networks and spatially distributed indicators for the use of a predictive model of such vulnerability. The analyses are complemented by a Monte Carlo evaluation of the uncertainty associated with the projections of the global circulation models, including how such uncertainty impacts the vulnerability forecast. To test the proposal, the paper uses current and future vulnerabilities of the Turbio River watershed, located in the semi-arid zone of Guanajuato (Mexico). The results show that nearly 50% of the watershed currently has medium and high vulnerabilities, and only the natural areas in the watershed show low vulnerabilities. In the future, an increase from medium to high vulnerability is expected to occur in urban and agricultural areas of the basin, with an associated uncertainty of ±15 mm in the projected precipitation. [ABSTRACT FROM AUTHOR]

Although the surface energy budget is essential to determine Earth's climate, site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations. This has led to the development of a growing number of surface radiation products, mainly including remotely sensed data, model reanalysis data, and simulations using General Circulation Models (GCMs). This collection of papers introduces new techniques, including the use of machine learning methods for radiation estimation, and evaluates and compares various radiation products, as well as their spatio-temporal variations. These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations. However, remotely sensed radiation products perform relatively better than others. Despite this, there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates. Even though the major radiative parameters including downward shortwave radiation, net longwave radiation, and albedo, from most products show insignificant long-term variation trends on a global scale, only specific regions, such as the Yunnan-Kweichow Plateau (YKP) and regions with permafrost (i.e. Qinghai-Tibet Plateau and Arctic) and glaciers (i.e. Altai Mountains) exhibit remarkable trends. [ABSTRACT FROM AUTHOR]

Key message: Drought severely worsened till 2100 and eventually outplayed growth-enhancing CO2 fertilization turning productivity gains into losses for beech and fir. Most scenarios generated notable losses in profitability but economic tipping points were later than for productivity due to lag effects related to discounting. Time mixture of fir and shortening rotation can counteract economic risks under climate change, but requires early admixture and moderate establishment costs. Context: Adaptation strategies to climate change (CC) such as establishing mixed forests are often based on ecological understanding while economic rationale is often disregarded. Aims: This paper studies CC uncertainty on productivity and profitability of European beech (Fagus sylvatica L.) and Silver fir (Abies alba Mill.). Besides, the economic consequences to actively adapt beech forests by admixing Silver fir are investigated. Methods: We used the process-based forest growth model GOTILWA + to simulate RCP2.6, RCP4.5 and RCP8.5 climatic projection by the MPI-ESM-LR global circulation model (MPI-ESM-LR) with the CO2 fertilization effect (eCO2) switched on and off. We analysed the sensitivity of the land expectation value (LEV) on CC and economic parameters. Results: CC initially increased productivity, but declined after a tipping point (2040–2070) and later also profitability (2045–2100). RCP8.5 had positive, RCP2.6 negative and RCP4.5 neutral effects on LEV. Switching off eCO2 turned RCP8.5 from the most profitable to the least profitable scenario and the opposite for RCP2.6. CC generally reduced optimal rotation (Ropt) being scenario dependant, but comparatively more for fir than beech. Admixing fir created an economic benefit when implemented before stand age 50 of beech. This benefit was nullified with protection costs for browsing control (fencing or tree shelters). Conclusions: Economic parameters (not CC) were the major source of uncertainty stemming from discounting factors and establishment costs. Admixture of fir and shortening rotation can provide a solution to tackle economic and climate uncertainties, but requires early admixture and browsing control. [ABSTRACT FROM AUTHOR]

When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub‐cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid‐scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry‐to‐wet 10‐days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas. Plain Language Summary: In nature, cold pools are formed by cold air masses descending from the low to mid‐troposphere in thunderstorms. When these drafts reach the surface, they spread out horizontally. A manifestation of cold pools is the relatively high speed at the gust front, which can lift environmental air producing new convective cells. Moreover, depending on ambient conditions, the cold pools may help organize the new convective cells, increasing their aggregation and forming the so‐called mesoscale convective systems (MCSs). MCSs, which cover hundreds to thousands of km2, significantly impact the global scale circulation, energy budget, hydrological cycle, and population safety. Forecasting MCSs is challenging for global circulation models (GCM) due to the broad spectrum of scales of the involved atmospheric phenomenon. The computational limitations, at present and for some time to come, do not allow running in real‐time GCMs, which explicitly solves all relevant scales of motion. This paper describes a methodology to account for essential interplays between cold pools edges and moist convection to be applied in the GCMs of weather and climate forecasting. We show that the method improves the model simulation of the main types of MCSs over the Amazon Basin and other continental areas. Key Points: A model scheme for including effect of cold pools edges in triggering new convective cells and storm propagation is presentedThe scheme is coupled with a convection parameterization and applied in the modeling of moist convective systemsThe method improves the organization, longevity, propagation, and severity of the simulated mesoscale convective systems [ABSTRACT FROM AUTHOR]

Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM. [ABSTRACT FROM AUTHOR]

In this paper we develop a novel framework aimed to significantly reduce biases related to marine stratocumulus clouds in general circulation models (GCMs) while circumventing excessive computational cost requirements. Our strategy is to increase the horizontal resolution using a regionally refined mesh (RRM) over our region of interest in addition to using the Framework for Improvement by Vertical Enhancement (FIVE) to increase the vertical resolution only for specific physical processes that are important for stratocumulus. We apply the RRM off the coast of Peru in the southeastern Pacific, a region that climatologically contains the most marine stratocumulus in the subtropics. We find that our new modeling framework is able to replicate the results of our high-resolution benchmark simulation with much fidelity, while reducing the computational cost by several orders of magnitude. In addition, this framework is able to greatly reduce the long-standing biases associated with marine stratocumulus in GCMs when compared to the standard-resolution control simulation. [ABSTRACT FROM AUTHOR]

General circulation models are a useful tool in understanding the three dimensional structure of hot Jupiter and sub-Neptune atmospheres; however, understanding the validity of the results from these simulations requires an understanding of the artificial dissipation required for numerical stability. In this paper, we investigate the impact of the longitudinal filter and vertical 'sponge' used in the Met Office's unified model when simulating gaseous exoplanets. We demonstrate that excessive dissipation can result in counter-rotating jets and a catastrophic failure to conserve angular momentum. Once the dissipation is reduced to a level where a super-rotating jet forms, however, the jet and thermal structure are relatively insensitive to the dissipation, except in the nightside gyres where temperatures can vary by |$\sim 100\, \mathrm{K}$|⁠. We do find, however, that flattening the latitudinal profile of the longitudinal filtering alters the results more than a reduction in the strength of the filtering itself. We also show that even in situations where the temperatures are relatively insensitive to the dissipation, the vertical velocities can still vary with the dissipation, potentially impacting physical processes that depend on the local vertical transport. [ABSTRACT FROM AUTHOR]

Atmospheric radiation is one of the most important atmospheric physics, and its expensive computation cost severely restricts the numerical simulation of atmospheric general circulation models. Therefore, it is necessary to study an efficient radiation parameterization scheme. Due to the powerful computing power of GPU, more and more numerical models are being transplanted to GPU. The CUDA C version (CC-RRTMG_SW) of the rapid radiative transfer model for general circulation models (RRTMG) shortwave radiation scheme (RRTMG_SW) has successfully run on GPU, but its computing efficiency is not yet very high, and the performance potential of GPU computing needs to be realized further. This paper is dedicated to optimizing CC-RRTMG_SW and exploring its maximum computing performance on GPU. First, a three-dimensional acceleration algorithm for CC-RRTMG_SW is proposed. Then, some optimization methods, such as decoupling data dependency, optimizing memory access, and I/O optimization, are studied. Finally, the optimized version of CC-RRTMG_SW is developed, namely CC-RRTMG_SW++. The experimental results demonstrate that the proposed acceleration algorithm and performance optimization methods are effective. CC-RRTMG_SW++ achieved good acceleration effects on different GPU architectures, such as NVIDIA Tesla K20, K40, and V100. Compared to RRTMG_SW running on a single Intel Xeon E5-2680 v2 CPU core, CC-RRTMG_SW++ obtained a speedup of 99.09 × on a single V100 GPU without I/O transfer. Compared to CC-RRTMG_SW, the computing efficiency of CC-RRTMG_SW++ increased by 174.46%. [ABSTRACT FROM AUTHOR]

We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zanna, 2021, https://doi.org/10.1002/essoar.10506419.1) and evaluate the online performance in a different model from which it was previously tested. This parameterization is a deep convolutional network that predicts parameters for a stochastic model of subgrid momentum forcing by mesoscale eddies. We treat the parameterization as we would a conventional parameterization once implemented in the numerical model. This includes trying the parameterization in a different flow regime from that in which it was trained, at different spatial resolutions, and with other differences, all to test generalization. We assess whether tuning is possible, which is a common practice in GCM development. We find the parameterization, without modification or special treatment, to be stable and that the action of the parameterization to be diminishing as spatial resolution is refined. We also find some limitations of the machine learning model in implementation: (a) tuning of the outputs from the parameterization at various depths is necessary; (b) the forcing near boundaries is not predicted as well as in the open ocean; (c) the cost of the parameterization is prohibitively high on central processing units. We discuss these limitations, present some solutions to problems, and conclude that this particular ML parameterization does inject energy, and improve backscatter, as intended but it might need further refinement before we can use it in production mode in contemporary climate models. Plain Language Summary: This paper discusses how machine learning can be used to make climate models more accurate. Specifically, we import an existing machine learning model that predicts how small eddies (in the order of 10–100 km) in the ocean affect larger currents. We test this machine learning model in a different ocean circulation model than the one it was originally designed for, and found that it worked well. However, we also found some limitations: the model works differently at different depths in the ocean, and it does not work as well near the coasts of the ocean. We also found that the model takes a long time to run on normal computers. Overall, we concluded that the model is promising, but more work is needed to make it work well in realistic situations. Key Points: A stochastic‐deep learning model is implemented in an ocean circulation model, MOM6We evaluate the online performance of the stochastic‐deep learning model as a subgrid parameterizationWe identify certain limitations of the machine learned parameterization which otherwise has the potential to improve specific metrics [ABSTRACT FROM AUTHOR]

The ratio of O to N2 number densities (O/N2) at different altitudes is an important parameter in describing thermospheric neutral composition changes and their effects on the ionosphere during geomagnetic storms. However, storm‐induced vertical variations in O/N2 and its dependence on the O and N2 perturbations are still not fully understood. Here, the Thermosphere/Ionosphere Electrodynamics General Circulation Model simulations were used to investigate the responses of thermospheric composition at different pressure levels to the super geomagnetic storm occurred on November 20 and 21 in 2003. Our analysis shows that the behaviors of O/N2 perturbations on different pressure levels are similar above ∼180 km altitude. In the middle and low thermosphere of below ∼300 km, the storm‐time O/N2 decrease is mainly caused by a large reduction of O number density. However, N2 enhancement plays a vital role in O/N2 decreases in the upper thermosphere. The O/N2 enhancement is mainly attributed to the N2 decreases at all pressure levels. The changes of O and N2 number densities at a constant pressure level can be explained by the perturbations of their mass mixing ratio (mmr) and total mass density (ρ). The regions of the O/N2 decrease are characterized by the O mmr decrease and N2 mmr enhancement, whereas the regions of the O/N2 increase are characterized by the O mmr increase and N2 mmr decrease. The ρ value that shows the decrease globally at most pressure levels during the storm either enhance or reduce the O and N2 perturbations. Plain Language Summary: The column O/column N2 density ratio (∑O/N2) was usually used to describe thermospheric neutral composition responses to geomagnetic storms and the storm effects on ionospheric plasma density. However, thermospheric circulation changed considerably during the storm, resulting in discrepancies in composition at different altitudes. Additionally, the daytime electron density changes during geomagnetic storms are more related to those of local O/N2 at a given altitude, not the ∑O/N2. Therefore, it is important to fully understand the storm‐induced vertical variations in O, N2 and O/N2 perturbations. In this paper, the vertical variations in O/N2 and its dependence on the O and N2 perturbations during the 20–21 November 2003 storm are investigated by the numerical simulations. Our results shows that the behaviors of O and N2 perturbations depend much on the altitude, but those of O/N2 on different pressure levels are similar, especially above ∼180 km. This study helps us better understand the physical process of storm‐time ∑O/N2 variations based on the observations. Key Points: In middle and low thermosphere of below ∼300 km, storm‐time decreases of the ratio of O/N2 volume density are mainly caused by O reductionIn the upper thermosphere, N2 enhancement plays a vital role in the decreases of the ratio of O/N2 volume density during the stormAt all pressure levels, storm‐time increases of the ratio of O/N2 volume density depend more on the N2 decreases [ABSTRACT FROM AUTHOR]

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]

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]

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]