Coarse-resolution General Circulation Model Research Articles

A Simple Lagrangian Parcel Model for the Initiation of Summer-time Mesoscale Convective Systems over the Central United States

AbstractMesoscale convective systems (MCSs) account for more than 50% of summer-time precipitation over the central United States (US) and have a significant impact on local weather and hydrologic cycle. It is hypothesized that the inadequate treatment of MCSs is responsible for the longstanding warm and dry bias over the central US in coarse-resolution general circulation model (GCM) simulations. In particular, a better understanding of MCS initiation is still lacking. Here a single-column Lagrangian parcel model is first developed to simulate the basic features of a rising parcel. This simple model demonstrates the collective effects of boundary layer moistening and dynamical lifting in triggering convective initiation and reproduces successfully its early afternoon peak with surface equivalent potential temperature as a controlling factor. It also predicts that convection is harder to trigger in the future climate under global warming, consistent with the results from convection-permitting regional climate simulations. Then a multi-column model that includes an array of single-column models aligned in the east-west direction and incorporates idealized cold pool interaction mechanisms is developed. The multi-column model captures readily the cold pool induced upscale growth feature in MCS genesis from initially scattered convection that is organized into a mesoscale cluster in a few hours. It also highlights the crucial role of lifting effects due to cold pool collision and spreading, subsidence effect, and gust front propagation speed in controlling the final size of mesoscale clusters and cold pool regions. This simple model should be useful for understanding fundamental mechanisms of MCS initiation and providing guidance for improving MCS simulations in GCMs.

The performance of regional climate models driven by various general circulation models in reproducing observed rainfall over East Africa

Regional climate models (RCM) are commonly used to downscale the coarse resolution general circulation models (GCMs) to produce climate variables at spatially high-resolution grids. The quality of the downscaled data depends on the skills of both GCMs and RCMs. In this study, 10 GCMs are used to constrain the boundary and provide initial conditions of three RCMs. A total of 18 GCM-RCMs combinations are employed to produce simulations over East Africa (EA). The accuracy of simulated rainfalls is evaluated with respect to Climate Research Unit (CRU) rainfall to identify the best GCM-RCM combinations. Bias, root mean squared error (RMSE), correlation coefficient, and MAE-based model skill score have shown that MPI-REMO, MIROC-REMO, MPI-RCA4, IPSL-RCA4, CCCMA-RCA4, MOHC-CCLM, MOHC-REMO, and CNRM-RCA4 during spring season; ICHEC-REMO, MIROC-REMO, MOHC-REMO, MIROC-RCA4, CSIRO-RCA4, and MPI-REMO during autumn season; CSIRO-RCA4, MIROC-RCA4, CCCMA-RCA4, MIROC-REMO, CNRM-RCA4, and MOHC-RECA during boreal summer; and ICHEC-REMO, NOAA-RCA4, MOHC-REMO, MOHC-CCLM, MIROC-REMO, MPI-REMO, and IPSL-RCA4 during boreal winter season are the best performing GCM-RCM combination. It is also evident that the skills of the models are better in autumn than their skills in boreal spring and summer. Moreover, summer rain in EA is the most difficult for models to simulate. Comparison of annual mean with the CRU rainfall shows that MPI-REMO, MIROC-REMO, CSIRO-RCA4, MOHC-REMO, CCCma-RCA4, IPSL-RCA4, and CNRM-RCA4 are also the best GCM-RCM combinations as observed from strong significant spatial correlation, as well as low bias, RMSE, and positive skill score as high as 0.7. Therefore, the GCM-RCM combinations that exhibit superior performance over EA in most seasons as well as in capturing observed annual mean are CCCMA-RCA4, MIROC-REMO, MPI-REMO, IPSL-RCA4, CSIRO-RCA4, MOHC-REMO, and MIROC-RCA4. The difference in skills between models as well as variation of the same model skill both spatially and seasonally implies the role of several factors such as local topography, vegetation, and surface type as well as robustness of model physics in capturing small scale processes such as mesoscale convection in boreal summer (e.g., over Ethiopian highlands).

Comparing the Performance of Dynamical and Statistical Downscaling on Historical Run Precipitation Data over a Semi-Arid Region

Precise evaluations of climate model precipitation outputs are valuable for making decisions regarding agriculture, water resource, and ecosystem management. Many downscaling techniques have been developed in the past few years for projection of weather variables. We need to apply dynamical and statistical downscaling (DD and SD) to bridge the gap between the coarse resolution general circulation model (GCM) outputs and the need for high-resolution climate information over a semi-arid region. We compare the requirements of DD (RegCM4) and SD (Delta) approaches, evaluate the historical run of NNRP1 data in comparison with station data, and analyze the changes in wet days and precipitation values through both methods during 1990–2010. In this study, we did not want to use prediction data under different scenarios of climate change, and we have just applied observed data to assess the amount of precise of NNRP1 data, over the observed period. SD method requires less time and computing power than DD. The DD approach performs better over the evaluation period according to efficiency criteria. In general, the Pearson correlation in DD with observation data in evaluation period was higher than (r > 0.72 and R2 > 0.52) SD (r > 0.65 and R2 > 0.41) over three study stations. Similarly, MAE and NSE show better results from DD relative to SD. SD underestimates the number annual mean wet-days for all three stations examined. DD overestimates a number of annual mean wet-days, but with less deviation from the observed mean.

West African Monsoon: current state and future projections in a high-resolution AGCM

The West African Monsoon (WAM) involves the interaction of multi-scale processes ranging from planetary to cumulus scales, which makes it challenging for coarse resolution General Circulation Models to accurately simulate WAM. The present study evaluates the ability of the high-resolution (sim 25 km) Atmospheric General Circulation Model HiRAM to simulate the WAM and to analyze its future projections by the end of the 21st century. For the historical period, two AMIP-type simulations were conducted, one forced with observed SST from Hadley Center Sea Ice and Sea Surface Temperature dataset and the other forced with SST from the coarse resolution Earth System Model (ESM2M), which is the parent model of HiRAM, i.e. both models have the same dynamical core and similar physical parameterizations. The future projection, using the Representative Concentration Pathway 8.5 and SST from ESM2M is also conducted. A process-based evaluation is carried out to elucidate HiRAM’s ability to represent the key processes and multiscale dynamic features those define the WAM circulation. Compared to ESM2M, HiRAM better represents most of the key circulation elements at different scales, and thus more accurately represents the intensity and spatial distribution of the WAM rainfall. The position of the African easterly jet is considerably improved in HiRAM simulations, leading to the improved positioning of the WAM rainbelt and the two-cell structure of convection. The future projection of the WAM exhibits warming over the entire domain, decreasing precipitation over the southern Sahel, and increase of precipitation over the western Sahara.

An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment

Regional climate models (RCMs) are used to downscale the coarse resolution General Circulation Model (GCM) outputs to a finer resolution for hydrological impact studies. However, RCM outputs often deviate from the observed climatological data, and therefore need bias correction before they are used for hydrological simulations. While there are a number of methods for bias correction, most of them use monthly statistics to derive correction factors, which may cause errors in the rainfall magnitude when applied on a daily scale. This study proposes a sliding window based daily correction factor derivations that help build reliable daily rainfall data from climate models. The procedure is applied to five existing bias correction methods, and is tested on six watersheds in different climatic zones of India for assessing the effectiveness of the corrected rainfall and the consequent hydrological simulations. The bias correction was performed on rainfall data downscaled using Conformal Cubic Atmospheric Model (CCAM) to 0.5° × 0.5° from two different CMIP5 models (CNRM-CM5.0, GFDL-CM3.0). The India Meteorological Department (IMD) gridded (0.25° × 0.25°) observed rainfall data was considered to test the effectiveness of the proposed bias correction method. The quantile–quantile (Q–Q) plots and Nash Sutcliffe efficiency (NSE) were employed for evaluation of different methods of bias correction. The analysis suggested that the proposed method effectively corrects the daily bias in rainfall as compared to using monthly factors. The methods such as local intensity scaling, modified power transformation and distribution mapping, which adjusted the wet day frequencies, performed superior compared to the other methods, which did not consider adjustment of wet day frequencies. The distribution mapping method with daily correction factors was able to replicate the daily rainfall pattern of observed data with NSE value above 0.81 over most parts of India. Hydrological simulations forced using the bias corrected rainfall (distribution mapping and modified power transformation methods that used the proposed daily correction factors) was similar to those simulated by the IMD rainfall. The results demonstrate that the methods and the time scales used for bias correction of RCM rainfall data have a larger impact on the accuracy of the daily rainfall and consequently the simulated streamflow. The analysis suggests that the distribution mapping with daily correction factors can be preferred for adjusting RCM rainfall data irrespective of seasons or climate zones for realistic simulation of streamflow.

Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics

Indian summer monsoon rainfall extremes and their changing characteristics under global warming have remained a potential area of research and a topic of scientific debate over the last decade. This partially attributes to multiple definitions of extremes reported in the past studies and poor understanding of the changing processes associated with extremes. The later one results into poor simulation of extremes by coarse resolution General Circulation Models under increased greenhouse gas emission which further deteriorates due to inadequate representation of monsoon processes in the models. Here we use transfer function based statistical downscaling model with non-parametric kernel regression for the projection of extremes and find such conventional regional modeling fails to simulate rainfall extremes over India. In this conjuncture, we modify the downscaling algorithm by applying a robust regression to the gridded extreme rainfall events. We observe, inclusion of robust regression to the downscaling algorithm improves the historical simulation of rainfall extremes at a 0.25° spatial resolution, as evaluated based on classical extreme value theory methods, viz., block maxima and peak over threshold. The future projections of extremes during 2081–2100, obtained with the developed algorithm show no change to slight increase in the spatial mean of extremes with dominance of spatial heterogeneity. These changing characteristics in future are consistent with the observed recent changes in extremes over India. The proposed methodology will be useful for assessing the impacts of climate change on extremes; specifically while spatially mapping the risk to rainfall extremes over India.

PREDICTORS AND THEIR DOMAIN FOR STATISTICAL DOWNSCALING OF CLIMATE IN BANGLADESH

Reliable projection of future rainfall in Bangladesh is very important for the assessment of possible impacts of climate change and implementation of necessary adaptation and mitigation measures. Statistical downscaling methods are widely used for downscaling coarse resolution general circulation model (GCM) output at local scale. Selection of predictors and their spatial domain is very important to facilitate downscaling future climate projected by GCMs. The present paper reports the finding of the study conducted to identify the GCM predictors and demarcate their climatic domain for statistical downscaling in Bangladesh at local or regional scale. Twenty-six large scale atmospheric variables which are widely simulated GCM predictors from 45 grid points around the country were analysed using various statistical methods for this purpose. The study reveals that large-scale atmospheric variables at the grid points located in the central-west part of Bangladesh have the highest influence on rainfall. It is expected that the finding of the study will help different meteorological and agricultural organizations of Bangladesh to project rainfall and temperature at local scale in order to provide various agricultural or hydrological services.

A GCM with cloud microphysics and its MJO simulation

The present study examines the Madden and Julian oscillation (MJO) appearing in a general circulation model (GCM) with full representation of cloud microphysics at 50 km horizontal resolution, and the MJO is compared with those of GCMs with conventional convective parameterizations. The present coarse-resolution GCM requires modifications of several parameters of cloud microphysics and an additional vertical mixing process in the lower troposphere to simulate the MJO reasonably well. The GCM with cloud microphysics only produces the relatively small-scale precipitation scattered in the tropic. The shallow convection added in the GCM helps moisten the lower troposphere and enhances low-level moisture convergence, and thus large-scale cloud clusters are generated effectively, resulting in a better simulation of MJO.

North Atlantic Storm-Track Sensitivity to Warming Increases with Model Resolution

Abstract Mesoscale condensational heating can increase the sensitivity of modeled extratropical cyclogenesis to horizontal resolution. Here a pseudo global warming experiment is presented to investigate how this heating-enhanced sensitivity to resolution changes in a warmer and thus moister atmosphere. The Weather Research and Forecasting (WRF) Model with 120- and 20-km grid spacing is used to simulate current and future climates. It is found that the North Atlantic storm-track response to global warming is amplified at the higher model resolution. The most dramatic changes occur over the northeastern Atlantic, where resolution typical of current general circulation models (GCMs) results in a smaller global warming response in comparison with that in the 20-km simulations. These results suggest that caution is warranted when interpreting projections from coarse-resolution GCMs of future cyclone activity over the northeastern Atlantic.

The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

Abstract The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) because of the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations. One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large-scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep, and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth–death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation. In this work, the authors use for the first time the SMCM in a GCM. The authors build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR GCM to a simple multicloud model. The authors tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJOs that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.

GCMs with implicit and explicit representation of cloud microphysics for simulation of extreme precipitation frequency

The present study aims to develop a general circulation model (GCM) with improved simulation of heavy precipitation frequency by improving the representations of cloud and rain processes. GCMs with conventional convective parameterizations produce common bias in precipitation frequency: they overestimate light precipitation and underestimate heavy precipitation with respect to observed values. This frequency shift toward light precipitation is attributed here to a lack of consideration of cloud microphysical processes related to heavy precipitation. The budget study of cloud microphysical processes using a cloud-resolving model shows that the melting of graupel and accretion of cloud water by graupel and rain water are important processes in the generation of heavy precipitation. However, those processes are not expressed explicitly in conventional GCMs with convective parameterizations. In the present study, the cloud microphysics is modified to allow its implementation into a GCM with a horizontal resolution of 50 km. The newly developed GCM, which includes explicit cloud microphysics, produces more heavy precipitation and less light precipitation than conventional GCMs, thus simulating a precipitation frequency that is closer to the observed. This study demonstrates that the GCM requires a full representation of cloud microphysics to simulate the extreme precipitation frequency realistically. It is also shown that a coarse-resolution GCM with cloud microphysics requires an additional mixing process in the lower troposphere.

Comparing statistically downscaled simulations of Indian monsoon at different spatial resolutions

• Statistical downscaling of ISMR at multiple spatial resolutions. • Comparison of skill scores of downscaled projections at different resolutions. • Changes in multi-model uncertainty with lead time and resolution. • No significant change in signal to noise ratio with the change in resolutions. Impacts of climate change are typically assessed with fairly coarse resolution General Circulation Models (GCMs), which are unable to resolve local scale features that are critical to precipitation variability. GCM simulations must be downscaled to finer resolutions, through statistical or dynamic modelling for further use in hydrologic analysis. In this study, we use a linear regression based statistical downscaling method for obtaining monthly Indian Summer Monsoon Rainfall (ISMR) projections at multiple spatial resolutions, viz., 0.05°, 0.25° and 0.50°, and compare them. We use 19 GCMs of Coupled Model Intercomparison Project Phase 5 (CMIP5) suite and combine them with multi model averaging and Bayesian model averaging. We find spatially non-uniform changes in projections at all resolutions for both combinations of projections. Our results show that the changes in the mean for future time periods (2020s, 2050s, and 2080s) at different resolutions, viz., 0.05°, 0.25° and 0.5°, obtained with both Multi-Model Average (MMA) and Bayesian Multi-Model Average (BMA) are comparable. We also find that the model uncertainty decreases with projection times into the future for all resolutions. We compute Signal to Noise Ratio (SNR), which represents the climate change signal in simulations with respect to the noise arising from multi-model uncertainty. This appears to be almost similar at different resolutions. The present study highlight that, a mere increase in resolution by a way of computationally more expensive statistical downscaling does not necessarily contribute towards improving the signal strength. Denser data networks and finer resolution GCMs may be essential for producing usable rainfall and hydrologic information at finer resolutions in the context of statistical downscaling.

Modeling the Impacts of Future Climate Change on Irrigation over China: Sensitivity to Adjusted Projections

Abstract Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China.

Uncertainty resulting from multiple data usage in statistical downscaling

Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.

Improving Statistical Downscaling of General Circulation Models

We present a new method for the statistical downscaling of coarse-resolution General Circulation Model (GCM) fields to predict local climate change. Most atmospheric variables have strong seasonal cycles. We show that the prediction of the non-seasonal variability of maximum and minimum daily surface temperature is improved if the seasonal cycle is removed prior to the statistical analysis. The new method consists of three major steps. First, the average seasonal cycles of both predictands and predictors are removed. Second, a principal component-based multiple linear regression model between the deseasonalized predictands and predictors is developed and validated. Finally, the regression is used to make projections of future changes in maximum and minimum daily surface temperature at Shearwater, Nova Scotia. This projection is made using the local grid-scale variables of the Canadian General Circulation Model Version 3 (CGCM3) climate model as predictors. Our statistical downscaling method indicates significant skill in predicting the observed distribution of temperature using GCM predictors. Projections suggest minimum and maximum temperatures at Shearwater will be up to about five degrees warmer by 2100 under the current “business-as-usual” scenario. RÉSUMÉ [Traduit par la rédaction] Nous présentons une nouvelle méthode pour la réduction d'échelle statistique des champs des modèles de circulation générale (MCG) à faible résolution pour prévoir les changements du climat local. La plupart des variables atmosphériques ont des cycles saisonniers bien marqués. Nous démontrons que la prédiction de la variabilité non saisonnière de la température de surface quotidienne minimum et maximum est meilleure si on retranche le cycle saisonnier avant de procéder à l'analyse statistique. Voici les trois grandes étapes de cette nouvelle méthode. D'abord, nous retirons les cycles saisonniers moyens des prédictants et des prédicteurs. Ensuite, nous concevons et validons un modèle de régression linéaire multiple sur composantes principales entre les prédictants et les prédicteurs désaisonnalisés. Enfin, nous nous servons de la régression afin d'établir des projections pour les changements à venir dans la température de surface quotidienne minimum et maximum à Shearwater en Nouvelle-Écosse. Cette projection est établie au moyen des variables locales à l'échelle du maillage de la troisième version du modèle canadien de circulation générale (MCCG3). Notre méthode de réduction d'échelle statistique se révèle très efficace pour prédire la répartition observée de la température au moyen des prédicteurs du MCG. D'après les projections, les températures minimum et maximum à Shearwater connaîtront une augmentation d'environ cinq degrés d'ici 2100 dans le scénario actuel de type « statu quo ».

A Theory of the Interhemispheric Meridional Overturning Circulation and Associated Stratification

AbstractA quantitative theoretical model of the meridional overturning circulation and associated deep stratification in an interhemispheric, single-basin ocean with a circumpolar channel is presented. The theory includes the effects of wind, eddies, and diapycnal mixing and predicts the deep stratification and overturning streamfunction in terms of the surface forcing and other parameters of the problem. It relies on a matching among three regions: the circumpolar channel at high southern latitudes, a region of isopycnal outcrop at high northern latitudes, and the ocean basin between.The theory describes both the middepth and abyssal cells of a circulation representing North Atlantic Deep Water and Antarctic Bottom Water. It suggests that, although the strength of the middepth overturning cell is primarily set by the wind stress in the circumpolar channel, middepth stratification results from a balance between the wind-driven upwelling in the channel and deep-water formation at high northern latitudes. Diapycnal mixing in the ocean interior can lead to warming and upwelling of deep waters. However, for parameters most representative of the present ocean mixing seems to play a minor role for the middepth cell. In contrast, the abyssal cell is intrinsically diabatic and controlled by a balance between the deep mixing-driven upwelling and the residual of the wind-driven and eddy-induced circulations in the Southern Ocean.The theory makes explicit predictions about how the stratification and overturning circulation vary with the wind strength, diapycnal diffusivity, and mesoscale eddy effects. The predictions compare well with numerical results from a coarse-resolution general circulation model.

Importance of Circulation Changes to Atlantic Heat Storage Rates on Seasonal and Interannual Time Scales

Abstract Ocean heat budgets and transports are diagnosed to elucidate the importance of general circulation changes to Atlantic Ocean heat storage rates. The focus is on low- and midlatitude regions and on seasonal and interannual time scales. An estimate of the ocean state over 1993–2004, produced by a coarse-resolution general circulation model fit to observations via the method of Lagrange multipliers, is used. Meridional heat transports are first decomposed into contributions from time-mean and time-variable velocity and temperature and second from zonally symmetric baroclinic (overturning, including Ekman) and zonally asymmetric (gyre and other spatially correlated) circulations. Heat storage rates are then ascribed to ocean–atmosphere heat exchanges, diffusive mixing, and advective processes related to the various components of the meridional heat transport. Results show that seasonal heat storage changes generally represent a local response to surface heat inputs, but seasonal advective changes are also important near the equator. Interannual heat storage rate anomalies are mostly due to advection in tropical regions, whereas both surface heat fluxes and advection contribute at higher latitudes. Low-latitude advection can be primarily attributed to zonally symmetric baroclinic circulations, but temperature variations and zonally asymmetric flows can contribute elsewhere. A relationship between interannual heat storage rates in the equatorial Atlantic’s top 100 m and meridional heat transport associated with the zonally symmetric baroclinic flow is observed; however, due in part to the role of shallow advective processes at these latitudes, any direct relationship between sea surface temperature variability and heat transport changes associated with intermediate or deep meridional overturning circulations is not clear.

The MJO and Convectively Coupled Waves in a Coarse-Resolution GCM with a Simple Multicloud Parameterization

Abstract The adequate representation of the dominant intraseasonal and synoptic-scale variability in the tropics, characterized by the Madden–Julian oscillation (MJO) and convectively coupled waves, is still problematic in current operational general circulation models (GCMs). Here results are presented using the next-generation NCAR GCM—the High-Order Methods Modeling Environment (HOMME)—as a dry dynamical core at a coarse resolution of about 167 km, coupled to a simple multicloud parameterization. The coupling is performed through a judicious choice of heating vertical profiles for the three cloud types—congestus, deep, and stratiform—that characterize organized tropical convection. Important control parameters that affect the types of waves that emerge are the background vertical gradient of the moisture and the stratiform fraction in the multicloud parameterization, which set the strength of large-scale moisture convergence and unsaturated downdrafts in the wake of deep convection, respectively. Three numerical simulations using different moisture gradients and different stratiform fractions are considered. The first experiment uses a large moisture gradient and a small stratiform fraction and provides an MJO-like example. It results in an intraseasonal oscillation of zonal wavenumber 2, moving eastward at a constant speed of roughly 5 m s−1. The second uses a weaker background moisture gradient and a large stratiform fraction and yields convectively coupled Rossby, Kelvin, and two-day waves, embedded in and interacting with each other; and the third experiment combines the small stratiform fraction and the weak background moisture gradient to yield a planetary-scale (wavenumber 1) second baroclinic Kelvin wave. While the first two experiments provide two benchmark examples that reproduce several key features of the observational record, the third is more of a demonstration of a bad MJO model solution that exhibits very unrealistic features.

Statistical Downscaling of Precipitation Using Machine Learning with Optimal Predictor Selection

Various methods have been proposed to downscale the coarse resolution general circulation model (GCM) climatological variables to the fine-scale regional variables; however, fewer studies have been focused on the selection of GCM predictors. Additionally, the results obtained from one downscaling technique may not be robust and the uncertainties related to the downscaling scheme are not realized. To address these issues, the writers employed independent component analysis (ICA) for predictor selection that determines spatially independent GCM variables. Cross-validation of the independent components is employed to find the predictor combination that describes the regional precipitation over the upper Willamette basin with minimum error. These climate variables, along with the observed precipitation, are used to calibrate three downscaling models: multilinear regression (MLR), support vector machine (SVM), and adaptive-network-based fuzzy inference system (ANFIS). The presented method incorporates several GCM grids in the downscaling process that allows considering more predictors in the model calibration and removes the predictors correlation and dependence by ICA. Also, the study uses several downscaling techniques to develop an ensemble of precipitation time series that can be used in hydrologic climate impact assessment. The performance assessment of the results indicates that the procedure is successful in choosing the predictors for downscaling the GCM data both in monthly and seasonal timescales. The study shows that by choosing proper predictors the MLR model is an efficient method for precipitation downscaling.

Evidence for Enhanced Eddy Mixing at Middepth in the Southern Ocean

Abstract Satellite altimetric observations of the ocean reveal surface pressure patterns in the core of the Antarctic Circumpolar Current (ACC) that propagate downstream (eastward) but slower than the mean surface current by about 25%. The authors argue that these observations are suggestive of baroclinically unstable waves that have a steering level at a depth of about 1 km. Detailed linear stability calculations using a hydrographic atlas indeed reveal a steering level in the ACC near the depth implied by the altimetric observations. Calculations using a nonlinear model forced by the mean shear and stratification observed close to the core of the ACC, coinciding with a position where mooring data and direct eddy flux measurements are available, reveal a similar picture, albeit with added details. When eddy fluxes are allowed to adjust the mean state, computed eddy kinetic energy and eddy stress are close to observed magnitudes with steering levels between 1 and 1.5 km, broadly consistent with observations. An important result of this study is that the vertical structure of the potential vorticity (PV) eddy diffusivity is strongly depth dependent, implying that the diffusivity for PV and buoyancy are very different from one another. It is shown that the flow can simultaneously support a PV diffusivity peaking at 5000 m2 s−1 or so near the middepth steering level and a buoyancy diffusivity that is much smaller, of order 1000 m2 s−1, exhibiting less vertical structure. An effective diffusivity calculation, using an advected and diffused tracer transformed into area coordinates, confirms that the PV diffusivity more closely reflects the mixing properties of the flow than does the buoyancy diffusivity, and points explicitly to the need for separating tracer and buoyancy flux parameterizations in coarse-resolution general circulation models. Finally, implications for the eddy-driven circulation of the ACC are discussed.