dataesr/scientific-paragraphs-categorization huggingface dataset:
There is generally good agreement between GPCC and the other observational and reanalysis data, with a correlation above 0. 85 for all seasons and all datasets, a low RMSE, and a similar standard deviation. Subregion 3 (Fig. 4 ) is characterized by winter rainfall as opposed to summer rainfall in the other two subregions. Most models are able to adequately capture the interannual variation of the winter season (JJA) rainfall in this region and all but one model have a correlation with the observations of greater than 0. 60. The RMSE is less than 0. 3 in all cases, and the standard deviation is found to be close to that of GPCC, implying that the Here, 10 model results, the multimodel ensemble, four observational datasets (UDEL, CRU, GPCP, and TRMM), and one reanalysis dataset (ERA-Interim) are compared against GPCC observational data.
In other seasons the ensemble mean generally performs better than most models, with some models performing equally well (especially in JJA). The correlation of the reanalysis precipitation with GPCC is found to be similar to that of the multimodel ensemble, the RMSE is found to be below 0. 2 for all seasons and the standard deviation is found to be very close to that of the GPCC dataset with the exception of JJA where an underestimation of 40% is noted. There is generally good agreement between GPCC and the other observational data, with a correlation above 0. 85 for all seasons and all datasets, though other datasets are found to have a somewhat lower standard deviation. The seasonal cycle results in Fig. 5  (area-averaged monthly mean precipitation values) show that for subregions 1 and 2 there is an overall good agreement between the different observational datasets and the reanalysis data, and the models are able to capture the pattern seen in the observations.
For subregion 3 there is good agreement between the CRU, UDEL, and GPCC observational datasets in terms of both the seasonal pattern and the magnitude of precipitation. The TRMM and GPCP data (satellite and merged satellite-gauge dataset, respectively) show lower precipitation compared to the other observations for the wet winter months. The characteristically lower precipitation for the month of June is due to the shorter time period used for these two datasets  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) , with all the other datasets also found to have similarly lower values during this shorter time period for this subregion. Precipitation in the reanalysis data for subregion 3 during the wet winter months is also found to be slightly lower than in the observations. The individual model results for subregion 3 in Fig.
5  show that a number of models simulate a higher precipitation during the dry summer months, which leads the ensemble mean pattern to display a 1-month lag in the onset of the rainfall season in this subregion with respect to the observations. A wet precipitation bias in the southwest of South Africa is also noted for some models in the spatial distribution of precipitation for DJF, which is discussed in section 3d (Fig. 8 ). The discussion in that section concludes that for a number of models the summer rainfall regime may penetrate too far southwest into the winter rainfall region, perhaps because of an overestimation of convective rainfall in summer over the inland part of the region, leading to higher precipitation estimates during these months. We also note the models show a peak in rainfall in June and not July as seen in the reference datasets (except GPCP and TRMM), which is also discussed further in section 3d.
b. Comparison of observational datasets: An estimate of observational uncertainty The comparison of GPCC and other observational datasets in Figs. 2 3 4 shows that in all subregions there are seasons for which the area-averaged interannual variation of monthly mean precipitation across the different observational datasets can vary and in some cases the differences are large (e. g. , JJA season in subregion 1). To study whether there are spatial differences within each subregion that lead to these differences in the area averages, the correlation between the variation of monthly mean precipitation in GPCC and other datasets is calculated and shown in Fig. 6  in the form of correlation maps (only values which are found to be significant at the 5% level are plotted). The limited time period of 1998-2006 is chosen so that comparison against the TRMM satellite-based dataset can also be included.
Similar results are obtained using the period 1990-2006 (not shown). The results displayed in Fig. 6  show that there is overall good correlation between the variation of monthly mean precipitation of other observational datasets and GPCC, though there are areas within the chosen subregions for which the correlation is lower (albeit significant). Very similar results are obtained using TRMM as reference (not shown), confirming that GPCC does not show a systematic departure from other datasets across the region studied. A comparison of the standard deviations (Fig. 7 ) reveals differences of up to 1. 5 mm day 21 in subregions 1 and 2 depending on the dataset, with TRMM having a systematically lower standard deviation in the northern part of the domain, as it is known to have a dry bias in this area  (Nikulin et al.
2012). Within subregion 1, in the area of northern Mozambique, most other datasets have a lower standard deviation compared to GPCC. There is a high correlation between the gridded GPCC, GPCP, and UDEL datasets and station data available for this region (see appendix). However, the GPCC data have a slightly lower RMSE compared to GPCP, and UDEL is found to have a very small RMSE. Precipitation biases between the observational datasets for individual seasons (not shown) show lower precipitation during DJF (and MAM) in the CRU, TRMM, and UDEL data compared to GPCC for this region (northern Mozambique), with differences reaching up to 3 mm day 21. Although TRMM is known to have a dry bias in the area  (Nikulin et al. 2012) , it is likely that GPCC slightly overestimates precipitation for this particular region and the comparison against station data would imply that UDEL is closer to observations.
However, it should be noted that this does not affect the discussion of model performance for this area of the domain (section 3c), as the precipitation biases in the model results are greater than the differences between the observed datasets and a number of models are found to underestimate precipitation over northern Mozambique for all seasons, irrespective of the gauge-based reference dataset used (CRU, UDEL, or GPCC; not shown here). For other seasons the differences in precipitation between GPCC and other gauge-based datasets across most of Southern Africa are generally small and TRMM is found to have a dry bias over the southwestern part of the domain, especially during JJA, and the entire northern part of the domain during most seasons. It is not the aim of this paper to analyze these differences in detail, as this has been the subject of  Simmons et al.
(2010)   2013 ). However, it is important to note these spatial differences and highlight that care needs to be made when analyzing area averages. For certain regions a number of factors, such as the quantity and quality of available station data, the different interpolation techniques, or (in the case of satellite data) the gauge analysis products used in the adjustments, can contribute to a higher uncertainty in terms of the reliability and validity of the datasets  (Yin and Gruber 2010; Nikulin et al. 2012; Tapiador et al. 2012). The selection of reference data is becoming an important factor in model assessment as more ground-, space-, and reanalysis-based rainfall products become available, which all use different methods to produce precipitation data. For example, satellite-based rainfall datasets include among others the Tropical Applications of Meteorology using Satellite Data (TAMSAT;  Grimes et al.
1999)   (Janowiak and Xie 1999) ; and the GHCN station data. The Water and Global Change (WATCH) dataset is based on reanalysis and station data  (Weedon et al. 2011 ). It therefore becomes theoretically possible to achieve a reduction in rainfall bias of a particular regional model for a particular season over particular regions by selecting the closest observed precipitation product. We suggest, along with  Nikulin et al. (2012) , that downscaled rainfall data be validated against an ensemble of observational data so that the variability within these datasets can be taken into account.
c. Biases in seasonal precipitation patterns Model results for precipitation across Southern  Africa (1990 Africa ( -2006 ) are compared to GPCC in Fig. 8  for DJF. The intercomparison of model results shows that model performance is similar across all seasons, so only DJF is shown, which is the wet season for most of the region. Model results were also compared against other observational datasets for the same time period (CRU and UDEL; not shown) and the results presented and discussed in this section apply irrespective of the reference dataset used. When compared to GPCC, most models are found to undersimulate precipitation in the northeastern part of the domain and to a lesser extent in the north and northwestern parts (especially during MAM) and higher precipitation results are found in the southeastern part, especially over the Lesotho highlands and Drakensburg areas.
The WRF3. 1. 1 RCM and to a lesser extent PRECIS have a wet bias over most of Southern Africa, though WRF3. 1. 1 shows a slightly different pattern as it also has a widespread dry bias in the northwestern part of the domain. HIRHAM5 and CCLM4. 8 have the driest bias in the northeastern part of the domain. CRCM5 is found to perform best and has a spatial precipitation pattern which is largely consistent to that of GPCC for all seasons and the differences seen in Fig. 8  are on the same order or less than those seen in other models. An exception is JJA, where this model shows a slight wet bias of ;2 mm day 21 in the northeastern part of the domain when compared against any observational dataset, which covers the entire period 1990-2006 (GPCC, UDEL, or CRU). Overall, CRCM5 has results which are very close to the multimodel ensemble and depending on the season the model performs slightly better (DJF and SON) or slightly worst (JJA) than the ensemble model results.
5  and shows that most models to a greater or lesser extent underestimate precipitation during the wet winter period in the southwestern tip of Africa. This finding is independent of the comparison dataset used. It is interesting to note certain regional details where, in addition to the large-scale dynamics, local topography is also expected to challenge the models. The undersimulation of precipitation in the northeastern part of the domain seen in most model results during DJF and MAM is more pronounced over Lake Malawi, a key geomorphological feature that needs to be appropriately resolved. However, it is admittedly challenging to capture the large-scale evaporation-condensation-precipitation cycle taking place over Africa's third largest lake at the spatial resolution defined in CORDEX.
As already discussed, the comparison of the different gauge-based observational datasets per season does not show a systematic tendency toward lower or higher precipitation estimates in the GPCC data for Southern Africa. The only main difference is found in the northeastern part of the domain, where precipitation in GPCC is somewhat higher (not shown), as already discussed in section 3b. Nevertheless, this does not affect the models' relative performance and the discussion of Fig. 8. The only difference is that in the multimodel ensemble results the dry bias over Mozambique is less pronounced when comparing against CRU and UDEL data, as these datasets indicate less precipitation in this area. Hence, the tendency toward lower/higher precipitation estimates in the model results is related to difficulties in modeling the larger circulation patterns and for particular areas the complex topography and its impact on the microclimate, and it is not influenced by biases in the observations.
The differences in the simulation of the large-scale circulation that most likely lead to an underestimation of precipitation in the southwestern tip of the domain (winter rainfall region) during JJA compared to the GPCC (seen to a greater or lesser degree in all models), are most probably linked to a less pronounced northward shift of the westerlies during winter and possibly also a weaker magnitude of these overall. It is likely that this is also the case in the ERA-Interim data (an underestimation of precipitation is also noted in ERA-Interim compared to the other reference datasets), and therefore the anomaly maps for JJA cannot be used to confirm this. However, from Fig. 5  it is evident that over this region the all models place the rainfall maximum in June instead of July. Sea level pressure for each model, as well as the model ensemble average, was compared against ERA-Interim SLP for June and July.
During DJF there is a wet precipitation bias in the southwestern tip of the domain, which is likely a function of an anomalously deep subcontinental low pressure over the region being produced by the models. In Fig. 9  this is apparent in five of the models as well as the ensemble mean, indicating that the extent of the subtropical low is too far south. Wind anomalies at 850 hPa in these models suggest that too much moisture is advected into the region either from the interior of the country (northeast) or from the Agulhas Current (east). The combination of a low pressure system intruding into the region from the interior as well as moisture advection could introduce the wet rainfall bias through convective precipitation. In further studies, moisture data will be used to examine this hypothesis.
This would likely remove moisture from the atmospheric column that is necessary for rainfall over northern Mozambique. Unfortunately, because of data constraints described above, we could not test the model's ability to capture stability, vorticity, and moisture transport. It is interesting to note that similar precipitation bias patterns are found in the ERA-Interim data compared to GPCC. e. Interannual variability of summer precipitation associated with the ENSO A regional impact of El Niño-Southern Oscillation (ENSO) on Southern African rainfall has been elucidated in a number of studies. In particular, El Niño warm events are found to favor seasonal droughts in South Africa  (Lindesay 1988; Van Heerden et al. 1988; Reason et al. 2000) , especially since the 1970s  (Richard et al. 2000; Richard et al.
2001) , and La Niña cold events are linked to a positive rainfall anomaly  (Nicholson and Selato 2000). The season where the ENSO influence is found to be greatest varies across the region  (Nicholson and Selato 2000) , but since it is not the main aim of the paper to study the influence in detail the results presented here focus on DJF, the main rainfall season for most of Southern Africa. El Niño composite plots and La Niña composite plots are shown in Figs. 10  and 11 , respectively. In Fig. 10 , there is overall good agreement across the different observational datasets and ERA-Interim. In Fig. 11 , the agreement between ERA-Interim and observations is still good, though in the reanalysis data the extent of the wetter conditions during La Niña events in South Africa is perhaps too large.
a. Observation datasets The GPCC rainfall dataset was used as the reference dataset, although it is recognized that the area-averaged interannual variation of monthly mean precipitation across the different observational datasets varied and in some cases this variation was large. In some regions the observational differences were as large as the multimodel ensemble mean bias. This highlights the challenge in Africa of the lack of high quality observation datasets at suitable temporal and spatial resolution necessary for evaluating RCM simulations. With the introduction of many more satellite-based precipitation datasets as well as satellite/reanalysis-station merged observed datasets, the selection of observed datasets will likely play a role in assessing model bias. We concur with the suggestion of  Nikulin et al.
b. Model biases Regional model studies often involve tuning a regional model so that it has an optimal configuration over particular spatial domain to produce the best results with respect to some observed dataset. They can be tuned in terms of domain size, lateral boundary condition (LBC) forcing, horizontal and vertical resolution, and the atmospheric and surface physics (see  Cr etat et al. 2012). Some model setups have even been found to be seasonally sensitive  (Ishak et al. 2012). Usually these types of studies focus over a region of interest, or a country or local area and the models are optimized to get the most accurate results for this location and for a specific season. In studies over Africa, the African Monsoon Multidisciplinary Analysis (AMMA) program has focused over West Africa using models including RegCM3, WRF3.
Summary and conclusions In this study, the CORDEX RCMs were analyzed for their ability to capture and characterize rainfall patterns over Southern Africa region during the period 1990-2006. The ensemble consists of 10 different RCMs all run at ;50-km resolution, on a common grid and driven by ERA-Interim for the period 1989-2008. Data were provided on monthly time scale. Performance of the individual models and the ensemble average is evaluated seasonally in terms of correlation, variability, and root-mean-square error in three subregions that spanned tropical, subtropical, and midlatitude rainfall regimes. Model precipitation biases were examined spatially, and an attempt was made to relate these biases to circulation biases. We also investigate the rainfall response to the ENSO signal. We used the GPCC data as the reference dataset for an assessment using Taylor diagrams in three areaaveraged regions defined by rainfall regime.
This showed that in all three regions the ensemble average outperformed individual model results in all seasons for correlation, standard deviation and RMSE. In subregion 1, which primarily experiences tropical and subtropical rainfall, during the wet season (DJF) the models are able to capture the phase of interannual variability well and the ensemble mean is generally more closely correlated with the GPCC than the other observational datasets. However, most models tend to systematically underestimate the magnitude of the interannual variability relative to GPCC. In subregion 2, a primarily subtropical rainfall regime, the spread between the observational datasets is smaller in all seasons. As seen in subregion 1, there is also a systematic underestimation in the magnitude of the interannual variability relative to GPCC by most models in the wet season; however, here the ensemble mean has a lower correlation with the GPCC.
In subregion 3, which experiences winter rainfall, models again underestimate the magnitude of the interannual variability, a characteristic shared with the other observational datasets, but there is good correlation between the ensemble mean and the GPCC. In terms of individual models in this analysis, ARPEGE5. 1 generally performed better than other models and in many cases was comparable to the ensemble mean; however, the ensemble mean generally outperformed the results from individual models. It is also noted that the ensemble mean improves on the ERA-Interim in the north of the domain in removing the wet bias over Angola and lowers the magnitude of the dry bias over northern Mozambique. However, there is no improvement over the ERA-Interim rainfall over Lesotho and the surrounding highlands.
2010; Paeth et al. 2011). Nikulin et al. (2012)  point out that for many of the downscaling groups this was the first time downscaling over Africa where land-convection interactions play a leading role in defining details of the precipitation climate. The multimodel average generally outperforms any of the individual models with biases of similar magnitude to differences between the observational datasets and in some regions is lower than the reanalysis bias. This is largely a result of cancellation of opposite signed biases across the models and highlights the importance of working in the context of multimodel ensembles when simulating and assessing Southern African climate characteristics. The biases could be related to some degree to large-scale circulation features, but this was not properly quantified because of the unavailability of relevant data.
2009; Jones et al. 2011). We conclude that the regional models used here can be used for the assessment of future climate projections and also suggest that, through the ensemble mean, they are likely to improve on raw GCM diagnostic output such as rainfall as well as their prognostic drivers. from the South African Weather Service (SAWS) and the Instituto Nacional de Meteorologia (INAM). Mean monthly precipitation data, derived from daily records for the period 1990-2006 from 428 stations in South Africa and Mozambique, are compared against two gauge-based gridded observational datasets, GPCC and UDEL, and the GPCP satellite-gauge dataset. Station monthly mean data was exclusively computed for months without missing daily values which yielded generally high mean data availability ranging from 76% to 100% with an average of 98%.
The closest grid point used in the comparison is determined by the absolute minimum distance to the station. There is good agreement for all gridded data, with mean correlations of 0. 89 for GPCC, 0. 82 for GPCP, and 0. 78 for UDEL (Fig. A1 ). The mean RMSE (mm day 21 ) is found to be lowest on average for UDEL (0. 29) and slightly higher for GPCC (0. 49) and GPCP (0. 67). The GPCC and UDEL datasets partly share the same gauge stations and differences can arise from the different processing algorithms and the levels of data availability for given time periods. We note that the higher RMSE associated with lower correlation for some stations on the north side of the bay of Maputo in GPCC may be a result of occasional mismatching of extremely rainy months. As an example, November 2000, among all November months for 1990-2006, shows the relative largest difference between GPCC and stations with a strong underestimation of rainfall by GPCC over this sector.
UDEL also strongly underestimates rainfall for this month while GPCP captures the peak. Compared to other regions of the world, there are very few stations in certain regions of Southern Africa, which makes it difficult to quantify the uncertainties between the different observational datasets. Where there are areas in the region that have fewer stations that can be used in the production of the GPCC product the uncertainties would be larger with respect to other reference datasets. For the density of in situ rainfall records used by GPCC, please see Fig. 3  in  Nikulin et al. (2012). This example demonstrates that the gridded products generally represent rainfall over South Africa and Mozambique well, as they partly share the same gauge stations. However, local differences do occur in areas where there are fewer stations and these differences can be important.
Cell suspension prepared for injection (2 l, corresponding to 200,000 cells) was kept on ice along the entire transplantation procedure and cell viability was assessed at the end of the session. The in vitro differentiation fate of the same iPSC-NP preparation was also assessed following dissociation and adhesion of single-cell suspension. As previously reported  [17] , 1 day after adhesion, all iPSC-NPs expressed the early neural progenitor marker nestin. Undifferentiated iPSCs labeled with an anti-OCT4 antibody were not detected in these cultures (data not shown).