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We assess the ability of the DePreSys3 prediction system to predict austral summer precipitation (DJF) over southern Africa, defined as the African continent south of 15°S. DePresys3 is a high resolution prediction system (at a horizontal resolution of ~ 60 km in the atmosphere in mid-latitudes and of the quarter degree in the Ocean) and spans the long period 1959–2016. We find skill in predicting interannual precipitation variability, relative to a long-term trend; the anomaly correlation skill score over southern Africa is greater than 0.45 for the first summer (i.e. lead month 2–4), and 0.37 over Mozambique, Zimbabwe and Zambia for the second summer (i.e. lead month 14–16). The skill is related to the successful prediction of the El-Nino Southern Oscillation (ENSO), and the successful simulation of ENSO teleconnections to southern Africa. However, overall skill is sensitive to the inclusion of strong La-Nina events and also appears to change with forecast epoch. For example, the skill in predicting precipitation over Mozambique is significantly larger for the first summer in the 1990–2016 period, compared to the 1959–1985 period. The difference in skill in predicting interannual precipitation variability over southern Africa in different epochs is consistent with a change in the strength of the observed teleconnections of ENSO. After 1990, and consistent with the increased skill, the observed impact of ENSO appears to strengthen over west Mozambique, in association with changes in ENSO related atmospheric convergence anomalies. However, these apparent changes in teleconnections are not captured by the ensemble-mean predictions using DePreSys3. The changes in the ENSO teleconnection are consistent with a warming over the Indian Ocean and modulation of ENSO properties between the different epochs, but may also be associated with unpredictable atmospheric variability. [ABSTRACT FROM AUTHOR]

The main states of the Angola Low (AL) are identified using clustering analysis applied to daily anomalous patterns of 700-hPa wind vorticity over Angola and adjacent countries from November to March for the 1980/81-2014/15 period. At the daily timescale, we examine the extent to which the main states of the AL modulate daily rainfall over southern Africa. At the interannual timescale, we assess both the relationship between the occurrence of these AL states and El Niño southern oscillation (ENSO) and the role of the AL in explaining ENSO's failure in driving southern African rainfall at times. Three reanalyses are considered to account for uncertainties induced by the scarcity of data available for assimilation over southern Africa. Three preferential states of the Angola Low are identified: AL state close to its seasonal climatology with slight zonal displacements, anomalously weak AL state and anomalously strong AL state with meridional displacements. These different states all significantly modulate daily southern African rainfall. Near-climatological AL state promotes wet rainfall anomalies over eastern subtropical southern Africa and dry rainfall anomalies over its western part. A slight westward shift in the near-climatological position of the AL leads to reversed zonal gradient in rainfall. The remaining regimes significantly modulate the meridional gradient in southern African rainfall. Anomalously weak and anomalously northward AL states promote wet rainfall anomalies over tropical southern Africa and dry rainfall anomalies over subtropical southern Africa. The reverse prevails for anomalously southward AL. At the interannual timescale, ENSO significantly modulates the seasonal occurrence of most AL states in the three reanalyses. Anomalously weak and southward AL states are more strongly correlated with regional rainfall than ENSO in all reanalyses, suggesting that accounting for AL variability may improve seasonal forecasts. Case study analysis of the major 1982/83 and 1997/98 El Niño events suggests that the weak rainfall anomalies and strong seasonal AL in 1997/98 may result from counteracting effects between ENSO and Indian Ocean coupled modes of variability. [ABSTRACT FROM AUTHOR]

Temporal and spatial variability of rainfall over Tunisia (at 12 km spatial resolution) is analyzed in a multi-year (1992-2011) ten-member ensemble simulation performed using the WRF model, and a sample of regional climate hindcast simulations from Euro-CORDEX. RCM errors and skills are evaluated against a dense network of local rain gauges. Uncertainties arising, on the one hand, from the different model configurations and, on the other hand, from internal variability are furthermore quantified and ranked at different timescales using simple spread metrics. Overall, the WRF simulation shows good skill for simulating spatial patterns of rainfall amounts over Tunisia, marked by strong altitudinal and latitudinal gradients, as well as the rainfall interannual variability, in spite of systematic errors. Mean rainfall biases are wet in both DJF and JJA seasons for the WRF ensemble, while they are dry in winter and wet in summer for most of the used Euro-CORDEX models. The sign of mean annual rainfall biases over Tunisia can also change from one member of the WRF ensemble to another. Skills in regionalizing precipitation over Tunisia are season dependent, with better correlations and weaker biases in winter. Larger inter-member spreads are observed in summer, likely because of (1) an attenuated large-scale control on Mediterranean and Tunisian climate, and (2) a larger contribution of local convective rainfall to the seasonal amounts. Inter-model uncertainties are globally stronger than those attributed to model's internal variability. However, inter-member spreads can be of the same magnitude in summer, emphasizing the important stochastic nature of the summertime rainfall variability over Tunisia. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of climate models in reproducing surface solar radiation (SSR) over Southern Africa (SA) by validating five Regional Climate Models (RCM, including CCLM4, HIRHAM5, RACMO22T, RCA4 and REMO2009) that participated in the Coordinated Regional Downscaling Experiment program over Africa (CORDEX-Africa) along with their ten driving General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 over SA. The model simulated SSR was thereby compared to reference data from ground-based measurements, satellite-derived products and reanalyses over the period 1990-2005. Results show that (1) the references obtained from satellite retrievals and reanalyses overall overestimate SSR by up to 10 W/m2 on average when compared to ground-based measurements from the Global Energy Balance Archive, which are located mainly over the eastern part of the southern African continent. (2) Compared to one of the satellite products (Surface Solar Radiation Data Set—Heliosat Edition 2; SARAH-2): GCMs overestimate SSR over SA in terms of their multi-model mean by about 1 W/m2 (compensation of opposite biases over sub-regions) and 7.5 W/m2 in austral summer and winter respectively; RCMs driven by GCMs show in their multimodel mean underestimations of SSR in both seasons with Mean Bias Errors (MBEs) of about − 30 W/m2 in austral summer and about − 14 W/m2 in winter compared to SARAH-2. This multi-model mean low bias is dominated by the simulations of the CCLM4, with negative biases up to − 76 W/m2 in summer and − 32 W/m2 in winter. (3) The discrepancies in the simulated SSR over SA are larger in the RCMs than in the GCMs. (4) In terms of trend during the "brightening" period 1990-2005, both GCMs and RCMs (driven by European Centre for Medium-Range Weather Forecasts Reanalysis ERA-Interim, short as ERAINT and GCMs) simulate an SSR trend of less than 1 W/m2 per decade. However, variations of SSR trend exist among different references data. (5) For individual RCM models, their SSR bias fields seem rather insensitive with respect to the different lateral forcings provided by ERAINT and various GCMs, in line with previous findings over Europe. (6) Biases in SSR are overall qualitatively consistent with those in total cloud cover. The information obtained in present study is of crucial importance for understanding future climate projections of SSR and for relevant impact studies. [ABSTRACT FROM AUTHOR]

This paper investigates and quantifies co-variability between large-scale convection in the South American and Southern African sectors at different timescales (interannual, intraseasonal and synoptic), during the austral summer seasons (November-February) from 1979 to 2012. Multivariate analyses (Canonical Correlation Analysis and Principal Component Analysis) are applied to daily outgoing longwave radiation (OLR, used as a proxy for atmospheric convection) anomalies to extract the principal modes of variability and co-variability in each and between both regions, filtered to consider the appropriate time-scales. At the interannual timescale, results confirm the predominant role of El Niño Southern Oscillation (ENSO), favoring enhanced convection over both southeastern Brazil and northern Argentina on the one hand, and tropical Africa and the western Indian Ocean on the other hand. At the intraseasonal timescale, the leading mode of co-variability is related to modulations of large-scale atmospheric convection over most of South America, and 10 days later, tropical Southern Africa. This mode accounts for the impacts of the Madden-Julian-oscillation (MJO) over these regions: identifying robust co-variability at the intraseasonal timescale between both regions require thus to consider a temporal shift between the two sectors. At the synoptic scale, however, co-variability consists mostly of a synchronous modulation of the large-scale atmospheric convection over the South American and Southern African sectors. This results from the development of concomitant Rossby waves forming a continuous wave train over the South Atlantic in the mid-latitudes, affecting both the South Atlantic and South Indian Convergence Zones. Among the days when convection shows significant anomalies (30 % of the total days in each sector), this synchronous mode occurs about 25 % of the time, individual Rossby waves modulating convection over one single region only during the remaining 75 % events. Another mode of co-variability, involving a single Rossby wave modulating the convection first over the Americas, and 4 days later over Africa, appears as sensibly weaker than the synchronous mode, suggesting that the 'wave train' mode occurs more frequently than the development and propagation of a single wave that could affect both regions. [ABSTRACT FROM AUTHOR]

This paper documents the capability of the ARW/WRF regional climate model to regionalize near-surface atmospheric variables at high resolution (8 km) over Burgundy (northeastern France) from daily to interannual timescales. To that purpose, a 20-year continuous simulation (1989-2008) was carried out. The WRF model driven by ERA-Interim reanalyses was compared to in situ observations and a mesoscale atmospheric analyses system (SAFRAN) for five near-surface variables: precipitation, air temperature, wind speed, relative humidity and solar radiation, the last four variables being used for the calculation of potential evapotranspiration (ET). Results show a significant improvement upon ERA-Interim. This is due to a good skill of the model to reproduce the spatial distribution for all weather variables, in spite of a slight over-estimation of precipitation amounts mostly during the summer convective season, and wind speed during winter. As compared to the Météo-France observations, WRF also improves upon SAFRAN analyses, which partly fail at showing realistic spatial distributions for wind speed, relative humidity and solar radiation-the latter being strongly underestimated. The SAFRAN ET is thus highly under-estimated too. WRF ET is in better agreement with observations In order to evaluate WRF's capability to simulate a reliable ET, the water balance of thirty Douglas-fir stands was computed using a process-based model. Three soil water deficit indexes corresponding to the sum of the daily deviations between the relative extractible water and a critical value of 40 % below which the low soil water content affects tree growth, were calculated using the nearest weather station, SAFRAN analyses weather data, or by merging observation and WRF weather variables. Correlations between Douglas-fir growth and the three estimated soil water deficit indexes show similar results. These results showed through the ET estimation and the relation between mean annual SWDI and Douglas-fir growth index that the main difficulties of the WRF model to simulate soil water deficit is mainly attributable to its precipitation biases. In contrast, the low discrepancies between WRF and observations for air temperature, wind speed, relative humidity and solar radiation make then usable for the water balance and ET computation. [ABSTRACT FROM AUTHOR]

This study presents an evaluation of the ability of 10 regional climate models (RCMs) participating in the COordinated Regional climate Downscaling Experiment-Africa to reproduce the present-day spatial distribution of annual cycles of precipitation over the South African region and its borders. As found in previous studies, annual mean precipitation is quasi-systematically overestimated by the RCMs over a large part of southern Africa south of about 20°S and more strongly over South Africa. The spatial analysis of precipitation over the studied region shows that in most models the distribution of biases appears to be linked to orography. Wet biases are quasi-systematic in regions with higher elevation with inversely neutral to dry biases particularly in the coastal fringes. This spatial pattern of biases is particularly obvious during summer and specifically at the beginning of the rainy season (November and December) when the wet biases are found to be the strongest across all models. Applying a k-means algorithm, a classification of annual cycles is performed using observed precipitation data, and is compared with those derived from modeled data. It is found that the in-homogeneity of the spatial and temporal distribution of biases tends to impact the modeled seasonality of precipitation. Generally, the pattern of rainfall seasonality in the ensemble mean of the 10 RCMs tends to be shifted to the southwest. This spatial shift is mainly linked to a strong overestimation of convective precipitation at the beginning of the rainy season over the plateau inducing an early annual peak and to an underestimation of stratiform rainfall in winter and spring over southwestern South Africa. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) model, driven laterally by ERA-Interim reanalyses, is used here to downscale rainfall, at relatively high resolution (~8 km) over Burgundy (eastern France), during the period 1989-2009. Regional simulations are compared to the Météo-France Station Network (MFSN; 127 daily rain-gauge records), at various temporal scales, including interannual variability, the annual cycle, and weather types. Results show that the spatial distribution of WRF-simulated rainfall climatology is consistent with MFSN observation data, but WRF tends to overestimate annual rainfall by ~+15 %. At the interannual scale, WRF also performs very well (r ~ 0.8), despite almost constant, systematic overestimation. Only the average annual rainfall cycle is not accurately reproduced by WRF (r ~ 0.5), with rainfall overestimation in spring and summer, when convective rainfall prevails. During the winter season (October-March), when stratiform rainfall is prevalent, WRF performs better. Despite the biases for summertime convective events, these results suggest that high-resolution WRF simulations could successfully be used to document present and future climate variability at a regional scale. Nevertheless, because of overestimated convective rainfall, WRF-simulated rainfall should probably not be used directly to feed impact models, especially during the vegetative summer period. [ABSTRACT FROM AUTHOR]

Using a large set of WRF ensemble simulations at 70-km horizontal resolution over a domain encompassing the Warm Pool region and its surroundings [45°N-45°S, 10°E-240°E], this study aims at quantifying how nudging techniques can modify the simulation of deep atmospheric convection. Both seasonal mean climate, transient variability at intraseasonal timescales, and the respective weight of internal (stochastic) and forced (reproducible) variability are considered. Sensitivity to a large variety of nudging settings (nudged variables and layers and nudging strength) and to the model physics (using 3 convective parameterizations) is addressed. Integrations are carried out during a 7-month season characterized by neutral background conditions and strong intraseasonal variability. Results show that (1) the model responds differently to the nudging from one parameterization to another. Biases are decreased by ~50 % for Betts-Miller-Janjic convection against 17 % only for Grell-Dévényi, the scheme producing yet the largest biases; (2) relaxing air temperature is the most efficient way to reduce biases, while nudging the wind increases most co-variability with daily observations; (3) the model's internal variability is drastically reduced and mostly depends on the nudging strength and nudged variables; (4) interrupting the relaxation before the end of the simulations leads to an abrupt convergence towards the model's natural solution, with no clear effects on the simulated climate after a few days. The usefulness and limitations of the approach are finally discussed through the example of the Madden-Julian Oscillation, that the model fails at simulating and that can be artificially and still imperfectly reproduced in relaxation experiments. [ABSTRACT FROM AUTHOR]

This study documents methodological issues arising when downscaling modes of large-scale atmospheric variability with a regional climate model, over a remote region that is yet under their influence. The retained case study is El Niño Southern Oscillation and its impacts on Southern Africa and the South West Indian Ocean. Regional simulations are performed with WRF model, driven laterally by ERA40 reanalyses over the 1971-1998 period. We document the sensitivity of simulated climate variability to the model physics, the constraint of relaxing the model solutions towards reanalyses, the size of the relaxation buffer zone towards the lateral forcings and the forcing fields through ERA-Interim driven simulations. The model's internal variability is quantified using 15-member ensemble simulations for seasons of interest, single 30-year integrations appearing as inappropriate to investigate the simulated interannual variability properly. The incidence of SST prescription is also assessed through additional integrations using a simple ocean mixed-layer model. Results show a limited skill of the model to reproduce the seasonal droughts associated with El Niño conditions. The model deficiencies are found to result from biased atmospheric forcings and/or biased response to these forcings, whatever the physical package retained. In contrast, regional SST forcing over adjacent oceans favor realistic rainfall anomalies over the continent, although their amplitude remains too weak. These results confirm the significant contribution of nearby ocean SST to the regional effects of ENSO, but also illustrate that regionalizing large-scale climate variability can be a demanding exercise. [ABSTRACT FROM AUTHOR]

This study aims at quantifying seasonal biases of regional climate model outputs during southern African summer, against a dense in situ measurement network (daily rain-gauge and surface air temperature records, and 12 h UTC radiosondes), and uncertainties associated with some physical parameterizations. Using the non-hydrostatic Advanced Research Weather Forecast (WRF) laterally forced by ERA40 reanalysis, twenty-seven experiments configured with three schemes of cumulus (CU), planetary boundary layer (PBL) and microphysics (MP), are performed at 35 km horizontal resolution during the core of a summer rainy season (December 1993 to February 1994 season) representative of the South African rainfall climatology. WRF simulates accurately seasonal large-scale rainfall patterns, as well as seasonal gradients of South African rainfall and 2-m temperature, and seasonal vertical profiles of the air temperature and humidity. However seasonal biases fluctuate strongly from an experiment to another, denoting considerable uncertainties generated by the physical package. Rainfall amounts are the most sensitive parameter to the tested schemes. Their geography, intensity, and intraseasonal characteristics are predominantly sensitive to CU schemes, and much less to PBL and MP schemes. Some CU-PBL combinations produce additive effects, which can dramatically either reduce or increase biases. Satisfactory configurations are found for South African climate, which would not have been possible without testing numerous physical parameterizations. [ABSTRACT FROM AUTHOR]

The CNRM atmospheric general circulation model Arpege-Climat is relaxed towards atmospheric reanalyses outside the 10°S-32°N 30°W-50°E domain in order to disentangle the regional versus large-scale sources of climatological biases and interannual variability of the West African monsoon (WAM). On the one hand, the main climatological features of the monsoon, including the spatial distribution of summer precipitation, are only weakly improved by the nudging, thereby suggesting the regional origin of the Arpege-Climat biases. On the other hand, the nudging technique is relatively efficient to control the interannual variability of the WAM dynamics, though the impact on rainfall variability is less clear. Additional sensitivity experiments focusing on the strong 1994 summer monsoon suggest that the weak sensitivity of the model biases is not an artifact of the nudging design, but the evidence that regional physical processes are the main limiting factors for a realistic simulation of monsoon circulation and precipitation in the Arpege-Climat model. Sensitivity experiments to soil moisture boundary conditions are also conducted and highlight the relevance of land-atmosphere coupling for the amplification of precipitation biases. Nevertheless, the land surface hydrology is not the main explanation for the model errors that are rather due to deficiencies in the atmospheric physics. The intraseasonal timescale and the model internal variability are discussed in a companion paper. [ABSTRACT FROM AUTHOR]

General circulation models still show deficiencies in simulating the basic features of the West African Monsoon at intraseasonal, seasonal and interannual timescales. It is however, difficult to disentangle the remote versus regional factors that contribute to such deficiencies, and to diagnose their possible consequences for the simulation of the global atmospheric variability. The aim of the present study is to address these questions using the so-called grid point nudging technique, where prognostic atmospheric fields are relaxed either inside or outside the West African Monsoon region toward the ERA40 reanalysis. This regional or quasi-global nudging is tested in ensembles of boreal summer simulations. The impact is evaluated first on the model climatology, then on intraseasonal timescales with an emphasis on North Atlantic/Europe weather regimes, and finally on interannual timescales. Results show that systematic biases in the model climatology over West Africa are mostly of regional origin and have a limited impact outside the domain. A clear impact is found however on the eddy component of the extratropical circulation, in particular over the North Atlantic/European sector. At intraseasonal timescale, the main regional biases also resist to the quasi-global nudging though their magnitude is reduced. Conversely, nudging the model over West Africa exerts a strong impact on the frequency of the two North Atlantic weather regimes that favor the occurrence of heat waves over Europe. Significant impacts are also found at interannual timescale. Not surprisingly, the quasi-global nudging allows the model to capture the variability of large-scale dynamical monsoon indices, but exerts a weaker control on rainfall variability suggesting the additional contribution of regional processes. Conversely, nudging the model toward West Africa suppresses the spurious ENSO teleconnection that is simulated over Europe in the control experiment, thereby emphasizing the relevance of a realistic West African monsoon simulation for seasonal prediction in the extratropics. Further experiments will be devoted to case studies aiming at a better understanding of regional processes governing the monsoon variability and of the possible monsoon teleconnections, especially over Europe. [ABSTRACT FROM AUTHOR]