Regional climate change experiments over southern South America. I: present climate

We present an analysis of a regional simulation of present-day climate (1981–1990) over southern South America. The regional model MM5 was nested within time-slice global atmospheric model experiments conducted by the HadAM3H model. We evaluate the capability of the model in simulating the observed climate with emphasis on low-level circulation patterns and surface variables, such as precipitation and surface air mean, maximum and minimum temperatures. The regional model performance was evaluated in terms of seasonal means, seasonal cycles, interannual variability and extreme events. Overall, the regional model is able to capture the main features of the observed mean surface climate over South America, its seasonal evolution and the regional detail due to topographic forcing. The observed regional patterns of surface air temperatures (mean, maxima and minima) are well reproduced. Biases are mostly within 3°C, temperature being overestimated over central Argentina and underestimated in mountainous regions during all seasons. Biases in northeastern Argentina and southeastern Brazil are positive during austral spring season and negative in other seasons. In general, maximum temperatures are better represented than minimum temperatures. Warm bias is larger during austral summer for maximum temperature and during austral winter for minimum temperature, mainly over central Argentina. The broad spatial pattern of precipitation and its seasonal evolution are well captured; however, the regional model overestimates the precipitation over the Andes region in all seasons and in southern Brazil during summer. Precipitation amounts are underestimated over the La Plata basin from fall to spring. Extremes of precipitation are better reproduced by the regional model compared with the driving model. Interannual variability is well reproduced too, but strongly regulated by boundary conditions, particularly during summer months. Overall, taking into account the quality of the simulation, we can conclude that the regional model is capable in reproducing the main regional patterns and seasonal cycle of surface variables. The present reference simulation constitutes the basis to examine the climate change simulations resulting from the A2 and B2 forcing scenarios which are being reported in a separate study.     

Regional climate simulations over South America: sensitivity to model physics and to the treatment of lateral boundary conditions using the MM5 model

In this study the capability of the MM5 model in simulating the main mode of intraseasonal variability during the warm season over South America is evaluated through a series of sensitivity experiments. Several 3-month simulations nested into ERA40 reanalysis were carried out using different cumulus schemes and planetary boundary layer schemes in an attempt to define the optimal combination of physical parameterizations for simulating alternating wet and dry conditions over La Plata Basin (LPB) and the South Atlantic Convergence Zone regions, respectively. The results were compared with different observational datasets and model evaluation was performed taking into account the spatial distribution of monthly precipitation and daily statistics of precipitation over the target regions. Though every experiment was able to capture the contrasting behavior of the precipitation during the simulated period, precipitation was largely underestimated particularly over the LPB region, mainly due to a misrepresentation in the moisture flux convergence. Experiments using grid nudging of the winds above the planetary boundary layer showed a better performance compared with those in which no constrains were imposed to the regional circulation within the model domain. Overall, no single experiment was found to perform the best over the entire domain and during the two contrasting months. The experiment that outperforms depends on the area of interest, being the simulation using the Grell (Kain–Fritsch) cumulus scheme in combination with the MRF planetary boundary layer scheme more adequate for subtropical (tropical) latitudes. The ensemble of the sensitivity experiments showed a better performance compared with any individual experiment.     

Uncertainty assessments of climate change projections over South America

This paper assesses the uncertainties involved in the projections of seasonal temperature and precipitation changes over South America in the twenty-first century. Climate simulations generated by 24 general circulation models are weighted according to the reliability ensemble averaging (REA) approach. The results show that the REA mean temperature change is slightly smaller over South America compared to the simple ensemble mean. Higher reliability in the temperature projections is found over the La Plata basin, and a larger uncertainty range is located in the Amazon. A temperature increase exceeding 2 °C is found to have a very likely (>90 %) probability of occurrence for the entire South American continent in all seasons, and a more likely than not (>50 %) probability of exceeding 4 °C by the end of this century is found over northwest South America, the Amazon Basin, and Northeast Brazil. For precipitation, the projected changes have the same magnitude as the uncertainty range and are comparable to natural variability.     

Simulation capability of tropical and extratropical seasonal climate anomalies over South America

An ensemble of 20 extended integrations of the atmospheric model CSIRO Mark 2, forced with the sea-surface temperature observed during the 1986–1998 period, was performed to analyze the simulation capability of seasonal climate anomalies over South America and adjacent oceanic areas. Variations of the simulation skill within the region and during the experimental period were assessed through standard statistical measures and compared to the signal-to-noise ratio distribution. Before the skill assessment, model systematic errors were thoroughly evaluated. The results confirm that the simulation skill is very high in tropical oceanic areas, and decreases rapidly towards middle and high latitudes. Model performance at mid and high atmospheric levels is substantially better than at low levels. Relatively high simulation capability was found over the Pacific Ocean between the equator and the Antarctic coast, which is coherent with the presence of three relative maximums in the signal-to-noise ratio, similar to the increase of the forced variance found by several authors over much of the Pacific–North American pattern region. Rainfall rate and second-order moments associated with the cyclonic activity and the meridional eddy fluxes of heat and humidity are better simulated in a narrow strip parallel to the SPCZ and extending further southeast into mid latitudes of the continent. The simulation skill noticeably improves during the warm and cold ENSO phases, in correspondence with an intensification of the signal-to-noise ratio, and useful rainfall anomaly simulations can be obtained over the Amazonas and Rio de la Plata river basins.     

Influence of ENSO and the South Atlantic Ocean on climate predictability over Southeastern South America

We perform a systematic study of the predictability of surface air temperature and precipitation in Southeastern South America (SESA) using ensembles of AGCM simulations, focusing on the role of the South Atlantic and its interaction with the El Niño-Southern Oscillation (ENSO). It is found that the interannual predictability of climate over SESA is strongly tied to ENSO showing high predictability during the seasons and periods when there is ENSO influence. The most robust ENSO signal during the whole period of study (1949–2006) is during spring when warm events tend to increase the precipitation over Southeastern South America. Moreover, the predictability shows large inter-decadal changes: for the period 1949–1977, the surface temperature shows high predictability during late fall and early winter. On the other hand, for the period 1978–2006, the temperature shows (low) predictability only during winter, while the precipitation shows not only high predictability in spring but also in fall. Furthermore, it is found that the Atlantic does not directly affect the climate over SESA. However, the experiments where air–sea coupling is allowed in the south Atlantic suggest that this ocean can act as a moderator of the ENSO influence. During warm ENSO events the ocean off Brazil and Uruguay tends to warm up through changes in the atmospheric heat fluxes, altering the atmospheric anomalies and the predictability of climate over SESA. The main effect of the air–sea coupling is to strengthen the surface temperature anomalies over SESA; changes in precipitation are more subtle. We further found that the thermodynamic coupling can increase or decrease the predictability. For example, the air–sea coupling significantly increases the skill of the model in simulating the surface air temperature anomalies for most seasons during period 1949–1977, but tends to decrease the skill in late fall during period 1978–2006. This decrease in skill during late fall in 1978–2006 is found to be due to a wrong simulation of the remote ENSO signal that is further intensified by the local air–sea coupling in the south Atlantic. Thus, our results suggest that climate models used for seasonal prediction should simulate correctly not only the remote ENSO signal, but also the local air–sea thermodynamic coupling.     

Regional climate simulations over Vietnam using the WRF model

Time-irreversible symmetry is a fundamental property of nonlinear time series. Time-irreversible behaviors of mean temperature measured on 182 stations over China from 1960 to 2012 are analyzed by directed horizontal visibility graph (DHVG for short), and significance of results has been estimated by Monte Carlo simulations. It is found that dominated time irreversibility emerges in nearly all daily temperature anomaly variations over China. Further studies indicate that these time-irreversible behaviors result from asymmetric distributions of persistent daily temperature increments and decrements, and this kind of symmetry can be quantified by distributions of consecutive daily mean temperature increasing or decreasing steps. At the same time, the findings above have been confirmed by artificially generated time series with given value of multiscale asymmetry.     

Regional climate change experiments over southern South America. II: Climate change scenarios in the late twenty-first century

We present an analysis of climate change over southern South America as simulated by a regional climate model. The regional model MM5 was nested within time-slice global atmospheric model experiments conducted by the HadAM3H model. The simulations cover a 10-year period representing present-day climate (1981–1990) and two future scenarios for the SRESA2 and B2 emission scenarios for the period 2081–2090. There are a few quantitative differences between the two regional scenarios. The simulated changes are larger for the A2 than the B2 scenario, although with few qualitative differences. For the two regional scenarios, the warming in southern Brazil, Paraguay, Bolivia and northeastern Argentina is particularly large in spring. Over the western coast of South America both scenarios project a general decrease in precipitation. Both the A2 and B2 simulations show a general increase in precipitation in northern and central Argentina especially in summer and fall and a general decrease in precipitation in winter and spring. In fall the simulations agree on a general decrease in precipitation in southern Brazil. This reflects changes in the atmospheric circulation during winter and spring. Changes in mean sea level pressure show a cell of increasing pressure centered somewhere in the southern Atlantic Ocean and southern Pacific Ocean, mainly during summer and fall in the Atlantic and in spring in the Pacific. In relation to the pressure distribution in the control run, this indicates a southward extension of the summer mean Atlantic and Pacific subtropical highs.     

Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model

The fifth-generation Canadian Regional Climate Model (CRCM5) was used to dynamically downscale two Coupled Global Climate Model (CGCM) simulations of the transient climate change for the period 1950–2100, over North America, following the CORDEX protocol. The CRCM5 was driven by data from the CanESM2 and MPI-ESM-LR CGCM simulations, based on the historical (1850–2005) and future (2006–2100) RCP4.5 radiative forcing scenario. The results show that the CRCM5 simulations reproduce relatively well the current-climate North American regional climatic features, such as the temperature and precipitation multiannual means, annual cycles and temporal variability at daily scale. A cold bias was noted during the winter season over western and southern portions of the continent. CRCM5-simulated precipitation accumulations at daily temporal scale are much more realistic when compared with its driving CGCM simulations, especially in summer when small-scale driven convective precipitation has a large contribution over land. The CRCM5 climate projections imply a general warming over the continent in the 21st century, especially over the northern regions in winter. The winter warming is mostly contributed by the lower percentiles of daily temperatures, implying a reduction in the frequency and intensity of cold waves. A precipitation decrease is projected over Central America and an increase over the rest of the continent. For the average precipitation change in summer however there is little consensus between the simulations. Some of these differences can be attributed to the uncertainties in CGCM-projected changes in the position and strength of the Pacific Ocean subtropical high pressure.     

Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties

The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American continent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990–2008 on a grid resolution of 50 km, following the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the seasonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub-regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of temperature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among individual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability.     

Evaluation of the regional climate model ALADIN to simulate the climate over North America in the CORDEX framework

In this study, an ensemble of four multi-year climate simulations is performed with the regional climate model ALADIN to evaluate its ability to simulate the climate over North America in the CORDEX framework. The simulations differ in their driving fields (ERA-40 or ERA-Interim) and the nudging technique (with or without large-scale nudging). The validation of the simulated 2-m temperature and precipitation with observationally-based gridded data sets shows that ALADIN performs similarly to other regional climate models that are commonly used over North America. Large-scale nudging improves the temporal correlation of the atmospheric circulation between ALADIN and its driving field, and also reduces the warm and dry summer biases in central North America. The differences between the simulations driven with different reanalyses are small and are likely related to the regional climate model’s induced internal variability. In general, the impact of different driving fields on ALADIN is smaller than that of large-scale nudging. The analysis of the multi-year simulations over the prairie and the east taiga indicates that the ALADIN 2-m temperature and precipitation interannual variability is similar or larger than that observed. Finally, a comparison of the simulations with observations for the summer 1993 shows that ALADIN underestimates the flood in central North America mainly due to its systematic dry bias in this region. Overall, the results indicate that ALADIN can produce a valuable contribution to CORDEX over North America.     

Evaluating uncertainties in regional climate simulations over South America at the seasonal scale

This work focuses on the evaluation of different sources of uncertainty affecting regional climate simulations over South America at the seasonal scale, using the MM5 model. The simulations cover a 3-month period for the austral spring season. Several four-member ensembles were performed in order to quantify the uncertainty due to: the internal variability; the definition of the regional model domain; the choice of physical parameterizations and the selection of physical parameters within a particular cumulus scheme. The uncertainty was measured by means of the spread among individual members of each ensemble during the integration period. Results show that the internal variability, triggered by differences in the initial conditions, represents the lowest level of uncertainty for every variable analyzed. The geographic distribution of the spread among ensemble members depends on the variable: for precipitation and temperature the largest spread is found over tropical South America while for the mean sea level pressure the largest spread is located over the southeastern Atlantic Ocean, where large synoptic-scale activity occurs. Using nudging techniques to ingest the boundary conditions reduces dramatically the internal variability. The uncertainty due to the domain choice displays a similar spatial pattern compared with the internal variability, except for the mean sea level pressure field, though its magnitude is larger all over the model domain for every variable. The largest spread among ensemble members is found for the ensemble in which different combinations of physical parameterizations are selected. The perturbed physics ensemble produces a level of uncertainty slightly larger than the internal variability. This study suggests that no matter what the source of uncertainty is, the geographical distribution of the spread among members of the ensembles is invariant, particularly for precipitation and temperature.     

LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection

Based on a decade of research on cloud processes, a new version of the LMDZ atmospheric general circulation model has been developed that corresponds to a complete recasting of the parameterization of turbulence, convection and clouds. This LMDZ5B version includes a mass-flux representation of the thermal plumes or rolls of the convective boundary layer, coupled to a bi-Gaussian statistical cloud scheme, as well as a parameterization of the cold pools generated below cumulonimbus by re-evaporation of convective precipitation. The triggering and closure of deep convection are now controlled by lifting processes in the sub-cloud layer. An available lifting energy and lifting power are provided both by the thermal plumes and by the spread of cold pools. The individual parameterizations were carefully validated against the results of explicit high resolution simulations. Here we present the work done to go from those new concepts and developments to a full 3D atmospheric model, used in particular for climate change projections with the IPSL-CM5B coupled model. Based on a series of sensitivity experiments, we document the differences with the previous LMDZ5A version distinguishing the role of parameterization changes from that of model tuning. Improvements found previously in single-column simulations of case studies are confirmed in the 3D model: (1) the convective boundary layer and cumulus clouds are better represented and (2) the diurnal cycle of convective rainfall over continents is delayed by several hours, solving a longstanding problem in climate modeling. The variability of tropical rainfall is also larger in LMDZ5B at intraseasonal time-scales. Significant biases of the LMDZ5A model however remain, or are even sometimes amplified. The paper emphasizes the importance of parameterization improvements and model tuning in the frame of climate change studies as well as the new paradigm that represents the improvement of 3D climate models under the control of single-column case studies simulations.     

Regional climate model sensitivity to domain size

Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere.     

Assessment of regional seasonal predictability using the PRECIS regional climate modeling system over South America

The purpose of this study was to evaluate the accuracy and skill of the UK Met Office Hadley Center Regional Climate Model (HadRM3P) in describing the seasonal variability of the main climatological features over South America and adjacent oceans, in long-term simulations (30 years, 1961–1990). The analysis was performed using seasonal averages from observed and simulated precipitation, temperature, and lower- and upper-level circulation. Precipitation and temperature patterns as well as the main general circulation features, including details captured by the model at finer scales than those resolved by the global model, were simulated by the model. However, in the regional model, there are still systematic errors which might be related to the physics of the model (convective schemes, topography, and land-surface processes) and the lateral boundary conditions and possible biases inherited from the global model.