Global General Circulation Model Research Articles

A New WENO‐Based Momentum Advection Scheme for Simulations of Ocean Mesoscale Turbulence

AbstractCurrent eddy‐permitting and eddy‐resolving ocean models require dissipation to prevent a spurious accumulation of enstrophy at the grid scale. We introduce a new numerical scheme for momentum advection in large‐scale ocean models that involves upwinding through a weighted essentially non‐oscillatory (WENO) reconstruction. The new scheme provides implicit dissipation and thereby avoids the need for an additional explicit dissipation that may require calibration of unknown parameters. This approach uses the rotational, “vector invariant” formulation of the momentum advection operator that is widely employed by global general circulation models. A novel formulation of the WENO “smoothness indicators” is key for avoiding excessive numerical dissipation of kinetic energy and enstrophy at grid‐resolved scales. We test the new advection scheme against a standard approach that combines explicit dissipation with a dispersive discretization of the rotational advection operator in two scenarios: (a) two‐dimensional turbulence and (b) three‐dimensional baroclinic equilibration. In both cases, the solutions are stable, free from dispersive artifacts, and achieve increased “effective” resolution compared to other approaches commonly used in ocean models.

Killing the predator: impacts of highest-predator mortality on the global-ocean ecosystem structure

Abstract. Recent meta-analyses suggest that microzooplankton biomass density scales linearly with phytoplankton biomass density, suggesting a simple, general rule may underpin trophic structure in the global ocean. Here, we use a set of highly simplified food web models, solved within a global general circulation model, to examine the core drivers of linear predator–prey scaling. We examine a parallel food chain model which assumes microzooplankton grazers feed on distinct size classes of phytoplankton and contrast this with a diamond food web model allowing shared microzooplankton predation on a range of phytoplankton size classes. Within these two contrasting model structures, we also evaluate the impact of fixed vs. density-dependent microzooplankton mortality. We find that the observed relationship between microzooplankton predators and prey can be reproduced with density-dependent mortality on the highest predator, regardless of choices made about plankton food web structure. Our findings point to the importance of parameterizing mortality of the highest predator for simple food web models to recapitulate trophic structure in the global ocean.

Fine Cloud Structures on a Hard Snowball Earth

AbstractEarth may have been globally ice‐covered at least two times during the Cryogenian Period (720–635 Ma). Previous studies showed that clouds could strongly warm a hard snowball Earth and promote its deglaciation. However, the understanding of clouds was largely based on coarse‐resolution global simulations with parameterized convection and clouds, or high‐resolution cloud‐resolving simulations with explicit convection and clouds but limited to a small domain without the effects of large‐scale circulation. Here we present the first global non‐hydrostatic high‐resolution (30 km) simulation to investigate snowball clouds and their radiative effects. We show that spatial‐temporal cloud distributions exhibit clear meanders (patterns of curved lines rather than straight lines along the west‐east direction) and strong variabilities, which result from the tight interactions among the Inter‐Tropical Convergence Zone, Hadley cells, and baroclinic instability. The net cloud radiative effect is around 20 W m−2 in the tropics, consistent with previous global general circulation model simulations.

Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California

Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.

An Efficient and Robust Estimation of Spatio‐Temporally Distributed Parameters in Dynamic Models by an Ensemble Kalman Filter

AbstractThe accuracy of Earth system models is compromised by unknown and/or unresolved dynamics, making the quantification of systematic model errors essential. While a model parameter estimation, which allows parameters to change spatio‐temporally, shows promise in quantifying and mitigating systematic model errors, the estimation of the spatio‐temporally distributed model parameters has been practically challenging. Here we present an efficient and practical method to estimate time‐varying parameters in high‐dimensional spaces. In our proposed method, Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE‐EnKF), model parameters estimated by EnKF are constrained by results of offline batch optimization, in which the posterior distribution of model parameters is obtained by comparing simulated and observed climatological variables. HOOPE‐EnKF outperforms the original EnKF in synthetic experiments using a two‐scale Lorenz96 model and a simple global general circulation model. One advantage of HOOPE‐EnKF over traditional EnKFs is that its performance is not greatly affected by inflation factors for model parameters, thus eliminating the need for extensive tuning of inflation factors. We thoroughly discuss the potential of HOOPE‐EnKF as a practical method for improving parameterizations of process‐based models and prediction in real‐world applications such as numerical weather prediction.

Bioclimatic modeling and FACE study forecast a bleak future for wheat production in India.

Since the impact of future climate change on wheat productivity isinconsistent, we studied geographic distribution and yield of wheat using two global General Circulation Models (GCMs) and Free Air CO2/O3 Enrichment (FACE) experiments. The GCMs (IPSL-CM5A-LR and NIMR-HADGEM2-AO) with four Representative Concentration Pathways (RCPs) and 19 bioclimatic variables were used for distribution/ecological niche modeling (ENM). Currently cultivated eight wheat cultivars were exposed to individual treatment of(i) ambient CO2, temperature, and ozone (ACO + AO + AT) representing the present climate scenario, and (ii) elevated CO2 (550ppm) (ECO), (iii) elevated temperature (+ 2°C) (ET), (iv) elevated O3 (ambient + 20ppb) (EO), (v) elevated CO2 + elevated O3 (ECO + EO), and (vi) elevated CO2 + elevated temperature + elevated O3 (ECO + EO + ET) under FACE facility simulating the future climate change scenarios in 2050. The niche models predicted a reduction in climatically suitable areas for wheat, and identified "maximum temperature" as the most influencing factorfor area reduction. The elevated CO2, O3, and temperature individually and in combinations had differential impacts on the yield of wheat cultivars. Only two cultivars, viz., DBW 184 and DBW 187 did not exhibit yield decline suggesting their suitability in the future climate change scenario. Since the performance of six out of eightcultivars significantly declined under simulated FACE experiment, and ENM predicted reduction in wheat cultivation area under RCP 8.5 in 2050,it was concluded that future of wheat cultivation in India is bleak. The study further indicates that coupling of bioclimatic modeling and FACE experiment can effectively predict the impact of climate change on different crops.

Climate services in Brazil: Past, present, and future perspectives

From the devastating effects of the 1877–1879 Great Drought in the Northeast region to the creation of the Center for Weather Forecast and Climate Studies (CPTEC) at the National Institute for Space Research (INPE) in the early 1990 s, Brazil went from a total absence of meteorological expertise to becoming a member of a select group of nations with the infrastructure and technical expertise to build and run a global general circulation model. This article reviews the most critical moments in the development of climate services in Brazil, addressing the evolution of its infrastructure for observation, monitoring, modeling, and prediction, the still incipient efforts in systematically understanding users’ perspectives and needs, and the work required to incorporate the usable science and co-production paradigms into the main centers of production of climate information. Advances and challenges are analyzed, and actions for strengthening the climate services framework are proposed.

Empirical evidence for deep convection being a major source of stratospheric ice clouds over North America

Abstract. Ice clouds in the lowermost stratosphere affect stratospheric water vapour and the Earth's radiation budget. The knowledge of its occurrence and driving forces is limited. To assess the distribution and possible formation mechanisms of stratospheric ice clouds (SICs) over North America, we analysed SIC occurrence frequencies observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) instrument during the years 2006 to 2018. Possible driving forces such as deep convection are assessed based on Atmospheric Infrared Sounder (AIRS) observations during the same time. Results show that at nighttime, SICs are most frequently observed during the thunderstorm season over the Great Plains from May to August (MJJA) with a maximum occurrence frequency of 6.2 %. During the months from November to February (NDJF), the highest SICs occurrence frequencies are 5.5 % over the north-eastern Pacific and western Canada and 4.4 % over the western North Atlantic. Occurrence frequencies of deep convection from AIRS, which includes storm systems, fronts, mesoscale convective systems, and mesoscale convective complexes at midlatitude and high latitude, show similar hotspots like the SICs, with highest occurrence frequencies being observed over the Great Plains in MJJA (4.4 %) and over the north-eastern Pacific, western Canada, and the western North Atlantic in NDJF (∼ 2.5 %). Both, seasonal patterns and daily time series of SICs and deep convection show a high degree of spatial and temporal relation. Further analysis indicates that the maximum fraction of SICs related to deep convection is 74 % over the Great Plains in MJJA and about 50 % over the western North Atlantic, the north-eastern Pacific, and western Canada in NDJF. We conclude that, locally and regionally, deep convection is the leading factor related to the occurrence of SICs over North America. In this study, we also analysed the impact of gravity waves as another important factor related to the occurrence of SICs, as the Great Plains is a well-known hotspot for stratospheric gravity waves. In the cases where SICs are not directly linked to deep convection, we found that stratospheric gravity wave observations correlate with SICs with as much as 30 % of the cases over the Great Plains in MJJA, about 50 % over the north-eastern Pacific and western Canada, and up to 90 % over eastern Canada and the north-west Atlantic in NDJF. Our results provide a better understanding of the physical processes and climate variability related to SICs and will be of interest for modellers as SIC sources such as deep convection and gravity waves are small-scale processes that are difficult to represent in global general circulation models.

Westward-Propagating Rossby Modes in Idealized GCMs

AbstractThis work investigates the characteristics of westward-propagating Rossby modes in idealized global general circulation models. Using a nonlinear smoothing algorithm to estimate the background spectrum and an objective method to extract the spectral peaks, the four leading meridional modes can be identified for each of the first three zonal wavenumbers, with frequencies close to the predictions from the Hough modes obtained by linearizing about a state of rest. Variations in peak amplitude for different modes, both within a simulation and across simulations, may be understood under the assumption that the forcing of the modes scales with the background spectrum. Surface friction affects the amplitude and width of the peaks but both remain finite as friction goes to zero, which implies that some other mechanism, arguably nonlinear, must also contribute to the damping of the modes. Although spectral peaks are also observed for the precipitation field with idealized moist physics, there is no evidence of mode enhancement by the convective heating. Subject to the same friction, the amplitude of the peaks are very similar in the dry and moist models when both are normalized by the background spectra.

Extreme sea levels in the Baltic Sea under climate change scenarios – Part 1: Model validation and sensitivity

Abstract. We analyze extreme sea levels (ESLs) and related uncertainty in an ensemble of regional climate change scenarios for the Baltic Sea. The ERA-40 reanalysis and five Coupled Model Intercomparison Project phase 5 (CMIP5) global general circulation models (GCMs) have been dynamically downscaled with the coupled atmosphere–ice–ocean model RCA4-NEMO (Rossby Centre regional atmospheric model version 4 – Nucleus for European Modelling of the Ocean). The 100-year return levels along the Swedish coast in the ERA-40 hindcast are within the 95 % confidence limits of the observational estimates, except those on the west coast. The ensemble mean of the 100-year return levels averaged over the five GCMs shows biases of less than 10 cm. A series of sensitivity studies explores how the choice of different parameterizations, open boundary conditions and atmospheric forcing affects the estimates of 100-year return levels. A small ensemble of different regional climate models (RCMs) forced with ERA-40 shows the highest uncertainty in ESLs in the southwestern Baltic Sea and in the northeastern part of the Bothnian Bay. Some regions like the Skagerrak, Gulf of Finland and Gulf of Riga are sensitive to the choice of the RCM. A second ensemble of one RCM forced with different GCMs uncovers a lower sensitivity of ESLs against the variance introduced by different GCMs. The uncertainty in the estimates of 100-year return levels introduced by GCMs ranges from 20 to 40 cm at different stations and includes the estimates based on observations. It is of similar size to the 95 % confidence limits of 100-year return levels from tide gauge records.

The influence of South American regional climate on the simulation of the Southern Hemisphere extratropical circulation

This paper presents new modeling evidence showing the added value of high-resolution information from South America (SA) in the simulation of the Southern Hemisphere (SH) extratropical circulation. LMDZ, a coarse-resolution atmospheric global general circulation model constitutes the main tool for this investigation. Parallel to the control simulation, a two-way nesting (TWN) simulation of LMDZ is performed with an interactive coupling to the same model, but with a higher-resolution zoom over SA. The third simulation is a perfect boundary simulation for which re-analysis information from ERA-Interim is used to nudge LMDZ, but only over SA. Results indicate that enhanced resolution over SA improves the representation of the most important processes that influence extratropical eddy activity. The local improvement is followed by a better representation of the global extratropical circulation, especially in austral summer. The regional climate enhancement over SA has positive effects on simulation of the midlatitude jet position during the austral summer by significantly reducing the bias of the mean zonal kinetic energy outside the nudged zone. On the other hand, the wintertime general circulation outside the nudged-zone shows a limited bias-reduction for the regional-driven simulations, especially in the case of the TWN system. However, improvements of the TWN system compared to the control experiment are noticed in early stages of cyclone lifecycle, as it is identified in a better simulation of transient meridional heat transport and transient kinetic energy intensity. The findings of the present study suggest, thus, that improvements in resolution over SA effectively excite the simulation of the mean atmospheric circulation in the SH.

The Impact of Modified Fractional Cloud Condensate to Precipitation Conversion Parameter in Revised Simplified Arakawa‐Schubert Convection Parameterization Scheme on the Simulation of Indian Summer Monsoon and Its Forecast Application on an Extreme Rainfall Event Over Mumbai

AbstractThe impact of modified fractional conversion parameter (from cloud condensate to precipitation) in the revised simplified Arakawa‐Schubert (RSAS) convection scheme in Climate Forecast System version 2 on the simulation of Indian summer monsoon (ISM) is examined. While the default fractional conversion parameter is constant in RSAS, the modified parameter has the form of an exponential function of temperature above the freezing level, whereas below the freezing, level it is kept constant. The model simulation indicates RSAS with modified conversion parameter (RSAS_mod) shows a better fidelity in capturing the mean monsoon features over the ISM region. The spatial distribution of precipitation shows notable improvement over the ISM region. Most of the global general circulation models has a tendency to grossly overestimate (underestimate) the convective (large‐scale) rainfall over the ISM region, which has somewhat improved in RSAS_mod simulation. It is suggested that reduced rate of conversion of cloud condensate to convective precipitation above the freezing level leads to suppression of convective precipitation, which further increases the detrained moisture from the upper‐level, resulting enhancement in large‐scale precipitation. Further, improvement has been noted in outgoing longwave radiation, wind circulation, total cloud fraction, and dynamical and thermodynamical processes in RSAS_mod simulation. The modified conversion parameter helps in improving the feedback between moisture and convective processes through better lower tropospheric moistening. In addition to mean summer monsoon, the RSAS_mod indicates its potential in predicting an extreme rainfall event over Mumbai in high‐resolution global forecast system at T1534 horizontal resolution. However, its fidelity needs to be further tested for more number of heavy rainfall events.

A Hybrid Coupled Ocean-Atmosphere Model and Its Simulation of ENSO and Atmospheric Responses

A new hybrid coupled model (HCM) is presented in this study, which consists of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model. The ocean component is the intermediate ocean model (IOM) of the intermediate coupled model (ICM) used at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS). The atmospheric component is ECHAM5, the fifth version of the Max Planck Institute for Meteorology atmospheric general circulation model. The HCM integrates its atmospheric and oceanic components by using an anomaly coupling strategy. A 100-year simulation has been made with the HCM and its simulation skills are evaluated, including the interannual variability of SST over the tropical Pacific and the ENSO-related responses of the global atmosphere. The model shows irregular occurrence of ENSO events with a spectral range between two and five years. The amplitude and lifetime of ENSO events and the annual phase-locking of SST anomalies are also reproduced realistically. Despite the slightly stronger variance of SST anomalies over the central Pacific than observed in the HCM, the patterns of atmospheric anomalies related to ENSO, such as sea level pressure, temperature and precipitation, are in broad agreement with observations. Therefore, this model can not only simulate the ENSO variability, but also reproduce the global atmospheric variability associated with ENSO, thereby providing a useful modeling tool for ENSO studies. Further model applications of ENSO modulations by ocean-atmosphere processes, and of ENSO-related climate prediction, are also discussed.

Uncertainties in Projections of the Baltic Sea Ecosystem Driven by an Ensemble of Global Climate Models

Many coastal seas worldwide are affected by human impacts such as eutrophication causing, inter alia, oxygen depletion and extensive areas of hypoxia. Depending on the region, global warming may reinforce these environmental changes by reducing air-sea oxygen fluxes, intensifying internal nutrient cycling and increasing river-borne nutrient loads. The development of appropriate management plans to effectively protect the marine environment requires projections of future marine ecosystem states. However, projections with regional climate models commonly suffer from shortcomings in the driving global General Circulation Models (GCMs). The differing sensitivities of GCMs to increased greenhouse gas concentrations affect regional projections considerably. In this study, we focused on one of the most threatened coastal seas, the Baltic Sea, and estimated uncertainties in projections due to climate model deficiencies and due to unknown future greenhouse gas concentration, nutrient load and sea level rise scenarios. To address the latter, simulations of the period 1975-2098 were performed using the initial conditions from an earlier reconstruction with the same Baltic Sea model (starting in 1850). To estimate the impacts of climate model uncertainties, dynamical downscaling experiments with four driving global models were carried out for two greenhouse gas concentration scenarios and for three nutrient load scenarios, covering the plausible range between low and high loads. The results of primary production, nitrogen fixation, and hypoxic areas show that uncertainties caused by the various nutrient load scenarios are greater than the uncertainties due to climate model uncertainties and future greenhouse gas concentrations. In all scenario simulations, a proposed nutrient load abatement strategy, i.e., the Baltic Sea Action Plan, will lead to a significant improvement in the overall environmental state. However, the projections cannot provide detailed information on the timing and the reductions of future hypoxic areas, due to uncertainties in salinity projections caused by uncertainties in projections of the regional water cycle and of the mean sea level outside the model domain.

Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.

Comparative terrestrial atmospheric circulation regimes in simplified global circulation models. Part II: Energy budgets and spectral transfers

The energetics of possible global atmospheric circulation patterns in an Earth‐like atmosphere are explored using a simplified global General Circulation Model (GCM) based on the University of Hamburg's Portable University Model for the Atmosphere (designated here as PUMA‐S), forced by linear relaxation towards a prescribed temperature field and subject to Rayleigh surface drag and hyperdiffusive dissipation. Results from a series of simulations, obtained by varying planetary rotation rate Ω with an imposed equator‐to‐pole temperature difference, were analysed to determine the structure and magnitude of the heat transport and other contributions to the energy budget for the time‐averaged, equilibrated flow. These show clear trends with rotation rate, with the most intense Lorenz energy cycle for an Earth‐sized planet occurring with a rotation rate around half that of the present‐day Earth (i.e., Ω∗ = Ω/Ω E = 1/2, where Ω E is the rotation rate of the Earth). Kinetic energy (KE) and available potential energy (APE) spectra, E K (n) and E A (n) (where n is total spherical wavenumber), also show clear trends with rotation rate, with n −3 enstrophy‐dominated spectra around Ω∗ = 1 and steeper (∼n −5) slopes in the zonal mean flow with little evidence for the n −5/3 spectrum anticipated for an inverse KE cascade. Instead, both KE and APE spectra become almost flat at scales larger than the internal Rossby radius, L d , and exhibit near‐equipartition at high wavenumbers. At Ω∗ < <1, the spectrum becomes dominated by KE with E K (n)∼(2–3)E A (n) at most wavenumbers and a slope that tends towards n −5/3 across most of the spectrum. Spectral flux calculations show that enstrophy and APE are almost always cascaded downscale, regardless of rotation rate. KE cascades are more complicated, however, with downscale transfers across almost all wavenumbers, dominated by horizontally divergent modes, for . At higher rotation rates, transfers of KE become increasingly dominated by rotational (horizontally nondivergent) components with strong upscale transfers (dominated by eddy–zonal flow interactions) for scales larger than L d and weaker downscale transfers for scales smaller than L d .

Deep learning to represent subgrid processes in climate models

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

Downscaling of Future Temperature and Precipitation Extremes in Addis Ababa under Climate Change

One of the recent advances in climate science research is the development of global general circulation models (GCMs) to simulate changes in climatic elements of the present and future, which helps us to determine consequences earlier and prepare for necessary adaptation measures. However, it is difficult to apply the raw data of GCMs at a local scale, such as the urban scale, without downscaling due to coarse resolution. This study, therefore, statistically downscaled daily maximum temperature, minimum temperature, and precipitation in 30-year intervals from the second generation of the Earth System Model (CanESM2) and Coupled Global Climate Model (CGCM3) under two Representative Concentration Pathways (RCP) Scenarios (RCP4.5 and RCP8.5) and two Special Report Emission Scenarios (SRES), A1B and A2, to examine future changes and their extremes. Two representative meteorological stations (Entoto at high elevation and Addis Ababa at downtown and medium elevation) were selected for model calibration and validation in the Statistical Downscaling Model (SDSM). Twelve core temperature and precipitation indices were selected to assess temperature changes and precipitation extremes. For the largest changes the results showed that the maximum temperature increases were in the range of 0.9 °C (RCP4.5) in 2020 to 2.1 °C (CGCM3A2) in 2080 at Addis Ababa Observatory. The minimum temperature is projected to increase by 0.3 °C (RCP4.5) in 2020 and 1.0 °C in 2080 (CGCM3A1B). While the changes in maximum temperature are lower at Entoto station compared to Addis Ababa Observatory, the highest minimum temperature change is projected at Addis Ababa Observatory, which ranges from 0.25 °C in the 2020s to 1.04 °C in 2080 according to the CGCM3 model. Except for the coldest nights (TNn), the mean temperature and other temperature indices will continue to increase to the end of this century. The highest precipitation change is projected by CGCM3A2 and CanESM2 RCP8.5 at an increase of about 11.8% and 16.62% by 2080. The highest total precipitation increase is 29% (RCP4.5) in winter and 20.9% (RCP8.5) in summer by 2080. There is high interseasonal variability in changes of extreme events. The topographic role will diminish in influence on the air temperature distribution due to the high rate of urbanization. The rise in temperature will exacerbate the urban heat highland effects in warm seasons and an increase in precipitation is expected along with a possible risk of flooding due to a low level of infrastructure development and a high rate of urbanization.

Boundary conditions for the Middle Miocene Climate Transition (MMCT v1.0)

Abstract. The Middle Miocene Climate Transition was characterized by major Antarctic ice sheet expansion and global cooling during the interval ∼ 15–13 Ma. Here we present two sets of boundary conditions for global general circulation models characterizing the periods before (Middle Miocene Climatic Optimum; MMCO) and after (Middle Miocene Glaciation; MMG) the transition. These boundary conditions include Middle Miocene global topography, bathymetry, and vegetation. Additionally, Antarctic ice volume and geometry, sea level, and atmospheric CO2 concentration estimates for the MMCO and the MMG are reviewed. The MMCO and MMG boundary conditions have been successfully applied to the Community Climate System Model version 3 (CCSM3) to provide evidence of their suitability for global climate modeling. The boundary-condition files are available for use as input in a wide variety of global climate models and constitute a valuable tool for modeling studies with a focus on the Middle Miocene.

The on-line coupled atmospheric chemistry model system MECO(n) – Part 5: Expanding the Multi-Model-Driver (MMD v2.0) for 2-way data exchange including data interpolation via GRID (v1.0)

Abstract. As part of the Modular Earth Submodel System (MESSy), the Multi-Model-Driver (MMD v1.0) was developed to couple online the regional Consortium for Small-scale Modeling (COSMO) model into a driving model, which can be either the regional COSMO model or the global European Centre Hamburg general circulation model (ECHAM) (see Part 2 of the model documentation). The coupled system is called MECO(n), i.e., MESSy-fied ECHAM and COSMO models nested n times. In this article, which is part of the model documentation of the MECO(n) system, the second generation of MMD is introduced. MMD comprises the message-passing infrastructure required for the parallel execution (multiple programme multiple data, MPMD) of different models and the communication of the individual model instances, i.e. between the driving and the driven models. Initially, the MMD library was developed for a one-way coupling between the global chemistry–climate ECHAM/MESSy atmospheric chemistry (EMAC) model and an arbitrary number of (optionally cascaded) instances of the regional chemistry–climate model COSMO/MESSy. Thus, MMD (v1.0) provided only functions for unidirectional data transfer, i.e. from the larger-scale to the smaller-scale models.Soon, extended applications requiring data transfer from the small-scale model back to the larger-scale model became of interest. For instance, the original fields of the larger-scale model can directly be compared to the upscaled small-scale fields to analyse the improvements gained through the small-scale calculations, after the results are upscaled. Moreover, the fields originating from the two different models might be fed into the same diagnostic tool, e.g. the online calculation of the radiative forcing calculated consistently with the same radiation scheme. Last but not least, enabling the two-way data transfer between two models is the first important step on the way to a fully dynamical and chemical two-way coupling of the various model instances.In MMD (v1.0), interpolation between the base model grids is performed via the COSMO preprocessing tool INT2LM, which was implemented into the MMD submodel for online interpolation, specifically for mapping onto the rotated COSMO grid. A more flexible algorithm is required for the backward mapping. Thus, MMD (v2.0) uses the new MESSy submodel GRID for the generalised definition of arbitrary grids and for the transformation of data between them.In this article, we explain the basics of the MMD expansion and the newly developed generic MESSy submodel GRID (v1.0) and show some examples of the abovementioned applications.