Precipitation Biases Research Articles

On the role of a coupled vegetation-runoff system in simulating the tropical African climate: a regional climate model sensitivity study

The role of vegetation-runoff system—in simulating the tropical African climate—was examined by analysing two 13-year simulations with two runoff schemes of the community land model version 4.5 (CLM45): the default one is TOPMODEL (TOP) and the other one is the Variable Infiltration Capacity (VIC) using a regional climate model (RegCM4-CLM45). In both simulations, the carbon-nitrogen (CN) module was activated. The first simulation was referred to as CN-TOP, while the second one was designated as CN-VIC. Overall, the results showed that the CN-VIC severely decreases the leaf area index (LAI), vegetation transpiration and soil evaporation relative to the CN-TOP. Eventually, it severely underestimates the total evapotranspiration but overestimates the sensible heat flux in comparison with the reanalysis product; meanwhile the CN-TOP opposes this effect. As a result, the CN-TOP shows a strong cold bias, and the CN-VIC shows a slightly warm bias in comparison with the observation. Moreover, enabling the interactive vegetation module leads to intensifying the dry bias of the total surface precipitation in both simulations with respect to the static vegetation case against the reanalysis product; however the CN-VIC still outperforms the CN-TOP in comparison with the observations. In conclusion, the coupled vegetation-runoff system has a strong influence on the tropical African climate relative to the static case, and calibrating the four parameters of the VIC surface dataset ensures a better and more reliable performance of the coupled RegCM4-CLM45-CN-VIC model for simulating the tropical African climate.

Comparison of Biases in Warm-Season WRF Forecasts in North and South America

AbstractEnsemble forecasts using the WRF Model at 20-km grid spacing with varying parameterizations are used to investigate and compare precipitation and atmospheric profile forecast biases in North and South America. By verifying a 19-member ensemble against NCEP Stage IV precipitation analyses, it is shown that the cumulus parameterization (CP), in addition to precipitation amount and season, had the largest influence on precipitation forecast skill in North America during 2016-2017. Verification of an ensemble subset against operational radiosondes in North and South America finds that forecasts in both continents feature a substantial mid-level dry bias, particularly at 700 hPa, during the warm season. Case-by-case analysis suggests that large mid-level error is associated with mesoscale convective systems (MCSs) east of the high terrain and westerly subsident flow from the Rocky and Andes Mountains in North and South America. However, error in South America is consistently greater than North America. This is likely attributed to the complex terrain and higher average altitude of the Andes relative to the Rockies, which allow for a deeper low-level jet and long-lasting MCSs, both of which 20-km simulations struggle to resolve. In the wake of data availability from the RELAMPAGO field campaign, the authors hope that this work motivates further comparison of large precipitating systems in North and South America, given their high impact in both continents.

Precipitation Biases in the ECMWF Integrated Forecasting System

AbstractPrecipitation is a key component of the global water cycle and plays a crucial role in flooding, droughts, and water supply. One way to manage its socioeconomic effects is based on precipitation forecasts from numerical weather prediction (NWP) models, and an important step to improve precipitation forecasts is by diagnosing NWP biases. In this study, we investigate the biases in precipitation forecasts from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS). Using the IFS control forecast from 12 June 2019 to 11 June 2020 at 5219 stations globally, we show that in each of the boreal winter and summer half years, the IFS 1) has an average global wet bias and 2) displays similar bias patterns for forecasts starting at 0000 and 1200 UTC and across forecast days 1–5. These biases are dependent on observed (climatological) precipitation; stations with low observed precipitation have an IFS wet bias, while stations with high observed precipitation have an IFS dry bias. Southeast Asia has a wet bias of 1.61 mm day−1 (in boreal summer) and over the study period the precipitation is overestimated by 31.0% on forecast day 3. This is the hydrological signature of several hypothesized processes including issues specifying the IFS snowpack over the Tibetan Plateau, which may affect the mei-yu front. These biases have implications for IFS land–atmosphere feedbacks, river discharge, and for ocean circulation in the Southeast Asia region. Reducing these biases could lead to more accurate forecasts of the global water cycle.

Evaluation of the Perspective of ERA-Interim and ERA5 Reanalyses for Calculation of Drought Indicators for Uzbekistan

The arid and semiarid regions of Uzbekistan are sensitive and vulnerable to climate change. However, the sparse and very unevenly distributed meteorological stations within the region provide limited data for studying the region’s climate variation. The aim of this work was to evaluate the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim and ERA5 products for the fields of near-surface temperature, humidity, and precipitation over Uzbekistan from 1981 to 2018 using observations from 74 meteorological stations. Major results suggested that the reanalysis datasets match well with most of the observed climate records, especially in the plain areas. While ERA5, with a high spatial resolution of 0.1°, is able more accurately reproduce mountain ranges and valleys. Compared to ERA-Interim, the climatological biases in temperature, humidity, and total precipitation from ERA5 are clearly reduced, and the representation of inter-annual variability is improved over most regions of Uzbekistan. Both reanalyses show a high level of agreement with observations on the standardized precipitation evaporation index (SPEI) with a correlation coefficient of 0.7–0.8.Although both of these ECMWF products can be successfully implemented for the calculation of atmospheric drought indicators for Uzbekistan and adjacent regions of Central Asia, the newer and advanced ERA5 is preferred.

Precipitation of Mainland India: Copula‐based bias‐corrected daily CORDEX climate data for both mean and extreme values

AbstractChanges in mean and extreme precipitation characteristics with changing climate may lead to an increase in frequency of hydrological extremes. For studying the impacts of the changing climate on hydrological systems, General Circulation Model (GCM)/Regional Climate Model (RCM) simulated precipitation are used. However, these products should be bias‐corrected before used in hydrological simulations to predict hydrological extremes. Most of the existing bias‐correction techniques suffer from either of two limitations – (a) they only reduce bias in selected precipitation quantile (either mean or extreme values), and/or (b) they exclude zero values from the analysis, even though their presence is significant in daily precipitation. In this study, a stochastic copula‐based bias‐correction method (Maity et al., J. Hydrometeorol., 20, 2019, 595), henceforth RMPH method, is used that corrects the bias in any quantile (mean and/or extreme values) of daily precipitation including zero values. The RMPH method is applied across Indian mainland to correct bias in simulated precipitation from the Coordinated Regional Climate Downscaling Experiment (CORDEX). Due to diverse climatic conditions across India, the quality of bias‐corrected precipitation is studied separately for different meteorologically homogenous regions of the country. Despite non‐uniform distribution of raingauge stations for observed precipitation, the superiority of the bias‐corrected precipitation (from RMPH method) in correcting bias and retaining the seasonal variation across the country is evident when compared with tradition bias‐correction approach like quantile mapping. The new bias‐corrected precipitation dataset developed is particularly suited for hydrological simulations, formulating extreme event mitigation strategies and climate change adaptation strategies.

An Assessment of Land–Atmosphere Interactions over South America Using Satellites, Reanalysis, and Two Global Climate Models

AbstractIn South America, land–atmosphere interactions have an important impact on climate, particularly the regional hydrological cycle, but detailed evaluation of these processes in global climate models has been limited. Focusing on the satellite-era period of 2003–14, we assess land–atmosphere interactions on annual to seasonal time scales over South America in satellite products, a novel reanalysis (ERA5-Land), and two global climate models: the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) and the U.K. Hadley Centre Global Environment Model version 3 (HadGEM3). We identify key features of South American land–atmosphere interactions represented in satellite and model datasets, including seasonal variation in coupling strength, large-scale spatial variation in the sensitivity of evapotranspiration to surface moisture, and a dipole in evaporative regime across the continent. Differences between products are also identified, with ERA5-Land, HadGEM3, and BAM-1.2 showing opposite interactions to satellites over parts of the Amazon and the Cerrado and stronger land–atmosphere coupling along the North Atlantic coast. Where models and satellites disagree on the strength and direction of land–atmosphere interactions, precipitation biases and misrepresentation of processes controlling surface soil moisture are implicated as likely drivers. These results show where improvement of model processes could reduce uncertainty in the modeled climate response to land-use change, and highlight where model biases could unrealistically amplify drying or wetting trends in future climate projections. Finally, HadGEM3 and BAM-1.2 are consistent with the median response of an ensemble of nine CMIP6 models, showing they are broadly representative of the latest generation of climate models.

Evaluation of Seasonal Forecasts for the Fire Season in Interior Alaska

AbstractIn this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.

Impacts of the radar data assimilation frequency and large-scale constraint on the short-term precipitation forecast of a severe convection case

Radar data assimilation (DA) can improve short-term precipitation forecasts. However, assimilating radar observations into a numerical prediction model may produce spurious precipitation and large errors in the position and magnitude of precipitation, and high-frequency cyclic assimilation may exacerbate this problem. In this paper, a severe convection case that occurred on 27 June 2018 over eastern China was used to investigate the impacts of the radar DA frequency and large-scale constraint on precipitation forecasts. The Weather Research and Forecasting (WRF) three-dimensional variational (3D-Var) DA system was used to assimilate radial velocity and reflectivity data. The sensitivity of the DA frequency using 15-min and 1-h assimilation intervals was examined first, revealing that the 15-min DA interval produced a greater overprediction bias of precipitation. A diagnosis of the analysis fields showed that higher-frequency DA cycling produced excessive wind speed and water vapor convergence at low levels and exaggerated upward movement. Then, the large-scale constraint was imposed on the regional model by the spectral nudging (SN) technique, which assimilated Global Forecast System (GFS) forecast field data during the forecast periods. The results demonstrated that the application of SN could significantly reduce the positional deviation of the precipitation forecast and the magnitude of overpredicted precipitation. SN could effectively adjust large-scale circulation fields, thereby improving the conditions of water vapor convergence. Moreover, SN also improved the forecasts of surface variables such as wind, temperature, and humidity.

Boundary condition and oceanic impacts on the atmospheric water balance in limited area climate model ensembles

Regional climate models (RCMs) are indispensable in climate research, albeit often characterized by biased terrestrial precipitation and water budgets. This study identifies excess oceanic evaporation, in conjunction with the RCMs’ boundary conditions, as drivers contributing to these biases in RCMs with forced sea surface temperatures in a CORDEX RCM ensemble over Europe. The RCMs are relaxed to the prescribed lateral boundary conditions originating from a global model, effectively matching the driving model's overall atmospheric moisture flux divergence. As a consequence, excess oceanic evaporation results in positive precipitation biases over land due to forced internal recycling of moisture to maintain the overall flux divergence prescribed by the boundary conditions. This systematic behaviour is shown through an analysis of long-term atmospheric water budgets and atmospheric moisture exchange between oceanic and continental areas in a multi-model ensemble.

The Importance of Scale‐Dependent Groundwater Processes in Land‐Atmosphere Interactions Over the Central United States

AbstractThis study explores the impacts of groundwater processes on the simulated land‐surface water balance and hydrometeorology. Observations are compared to multiscale Weather Research and Forecasting (WRF) simulations of three summer seasons: 2012, 2013, and 2014. Results show that a grid spacing of 3 km or smaller is necessary to capture small‐scale river and stream networks and associated shallow water tables, which supplies additional root‐zone water double that of simulations with 9‐km and 27‐km grid spacing and is critical to replenishing the depleted vegetation root zones and leads to 150 mm more evapotranspiration. Including groundwater processes in convection‐permitting models is effective to reduce: (1) 2‐m temperature warm biases from 5–6 to 2–3 °C and (2) the low precipitation bias by half. The additional groundwater supply to active soil flux in convection‐permitting simulations with groundwater for June‐August is nearly translated into the same amount of increased precipitation in the domain investigated.

Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau

AbstractGeneral circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

Biases in Indian Summer Monsoon Precipitation Forecasts in the Unified Model and Their Relationship With BSISO Index

AbstractThis study shows that the Boreal Summer Intraseasonal Oscillation (BSISO) dominates the Indian summer monsoon low‐precipitation bias in the Met Office Unified model. Analyzing a recent 9‐year period (June, July, August only), it is found that the precipitation bias is dominated by break and break‐to‐active transition BSISO phases, while some of the other phases have no bias at all over a 7‐day forecast. Evidence of a link to upstream effects is found, in that there is a delayed reduction in the moisture flux entering India from the west. It is also shown that an increase in the net flow of moisture out of India to the east is strongly linked to the low‐precipitation bias, and there is some evidence that this is related to a lack of low‐pressure systems over India. Most atmospheric models have substantial rainfall biases over India, and these results may indicate the circulation patterns responsible.

Systematic Patterns in Land Precipitation Due to Convection in Neighboring Islands in the Maritime Continent During MJO Propagation

AbstractThe land‐sea contrast in the Maritime Continent (MC) has been found to influence the Madden–Julian Oscillation (MJO). However, the specific contribution from individual islands to the precipitation over the surrounding islands during MJO propagation is not well known. We found that when an island is removed in the presence of lower‐tropospheric westerlies, precipitation increases over islands that are located to its east due to the strengthening of the westerlies. Frictional convergence of the stronger westerlies, aided by Coriolis, leads to an increase in vertical advection of moisture and precipitation over an island located to the east. On the other hand, the reduced heating over the removed island reduces the westerlies and precipitation to the west of the removed island and is consistent with the response of large‐scale circulation to tropical heating. During background easterlies prior to MJO arrival, a systematic decrease in precipitation was found in the surrounding islands to the west side of the removed island. But, on the eastern side of the removed island, no systematic change in precipitation was found. The results imply that changes in large‐scale circulation in response to convection (or a lack thereof) over a removed island may significantly influence precipitation in the neighboring islands. Therefore, biases in model precipitation over an island in the MC may arise from bias in precipitation over a neighboring island. Moreover, the presence of different island chains in the MC has led to a more conducive environment for more overall precipitation over the islands in the MC.

Future Changes in Seasonality in East Africa from Regional Simulations with Explicit and Parameterized Convection

AbstractThe East African precipitation seasonal cycle is of significant societal importance, and yet the current generation of coupled global climate models fails to correctly capture this seasonality. The use of convective parameterization schemes is a known source of precipitation bias in such models. Recently, a high-resolution regional model was used to produce the first pan-African climate change simulation that explicitly models convection. Here, this is compared with a corresponding parameterized-convection simulation to explore the effect of the parameterization on representation of East Africa precipitation seasonality. Both models capture current seasonality, although an overestimate in September–October in the parameterized simulation leads to an early bias in the onset of the boreal autumn short rains, associated with higher convective instability and near-surface moist static energy. This bias is removed in the explicit model. Under future climate change both models show the short rains getting later and wetter. For the boreal spring long rains, the explicit convection simulation shows the onset advancing but the parameterized simulation shows little change. Over Uganda and western Kenya both simulations show rainfall increases in the January–February dry season and large increases in boreal summer and autumn rainfall, particularly in the explicit convection model, changing the shape of the seasonal cycle, with potential for pronounced socioeconomic impacts. Interannual variability is similar in both models. Results imply that parameterization of convection may be a source of uncertainty for projections of changes in seasonal timing from global models and that potentially impactful changes in seasonality should be highlighted to users.

BCC-ESM1 Model Datasets for the CMIP6 Aerosol Chemistry Model Intercomparison Project (AerChemMIP)

BCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model, and is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved. All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted. The DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations have also been run as the entry card of CMIP6. The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies. To facilitate the use of the BCC-ESM1 datasets, this study describes the experiment settings and summarizes the model outputs in detail. Preliminary evaluations of BCC-ESM1 are also presented, revealing that: the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5; the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured, despite some common precipitation biases as in CMIP5 and CMIP6 models; a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1, as in most other ESMs; and the mean states of surface sulfate concentrations can also be reasonably reproduced, as well as their temporal evolution at regional scales. These datasets have been archived on the Earth System Grid Federation (ESGF) node for atmospheric chemistry studies.

Relating model bias and prediction skill in the equatorial Atlantic

We investigate the impact of large climatological biases in the tropical Atlantic on reanalysis and seasonal prediction performance using the Norwegian Climate Prediction Model (NorCPM) in a standard and an anomaly coupled configuration. Anomaly coupling corrects the climatological surface wind and sea surface temperature (SST) fields exchanged between oceanic and atmospheric models, and thereby significantly reduces the climatological model biases of precipitation and SST. NorCPM combines the Norwegian Earth system model with the ensemble Kalman filter and assimilates SST and hydrographic profiles. We perform a reanalysis for the period 1980–2010 and a set of seasonal predictions for the period 1985–2010 with both model configurations. Anomaly coupling improves the accuracy and the reliability of the reanalysis in the tropical Atlantic, because the corrected model enables a dynamical reconstruction that satisfies better the observations and their uncertainty. Anomaly coupling also enhances seasonal prediction skill in the equatorial Atlantic to the level of the best models of the North American multi-model ensemble, while the standard model is among the worst. However, anomaly coupling slightly damps the amplitude of Atlantic Niño and Niña events. The skill enhancements achieved by anomaly coupling are largest for forecast started from August and February. There is strong spring predictability barrier, with little skill in predicting conditions in June. The anomaly coupled system show some skill in predicting the secondary Atlantic Niño-II SST variability that peaks in November–December from August 1st.

Intercomparison of Bias-Correction Data Sources and Their Influence on Watershed-Specific Downscaling Climate Projections

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

Future climatic and hydrologic changes estimated by bias-adjusted regional climate model outputs of the Cordex-Africa project: case of the Tafna basin (North-Western Africa)

This study investigates climatic and hydrologic changes of the Tafna basin, by using ten outputs of precipitation and temperature from RCMs of the Cordex-Africa project. Different methods of bias-correction (LS, LOCI, DM and VS) are compared to correct the bias of precipitation and temperature datasets to observations. The suitable method, DM, reduces the bias to 1.27 mm for precipitation and 0.06 and 0.7°C for minimum/maximum temperature, respectively. The bias-corrected precipitation and temperature datasets are introduced into the SWAT model, calibrated and validated on the Tafna basin with good Nash criteria (NSEoutlet = 0.83). The discharge is over or under-estimated without bias-correction of RCM outputs, which highlights the necessity of applying bias-correction before using RCM outputs from Cordex-Africa for hydrological applications. The results show that the precipitation and discharge decreases, and temperature increases are more important with RCP 8.5 than with RCP 4.5, especially in the last decades of the 21st century.

Future climatic and hydrologic changes estimated by bias-adjusted regional climate model outputs of the Cordex-Africa project: case of the Tafna basin (North-Western Africa)

This study investigates climatic and hydrologic changes of the Tafna basin, by using ten outputs of precipitation and temperature from RCMs of the Cordex-Africa project. Different methods of bias-correction (LS, LOCI, DM and VS) are compared to correct the bias of precipitation and temperature datasets to observations. The suitable method, DM, reduces the bias to 1.27 mm for precipitation and 0.06 and 0.7°C for minimum/maximum temperature, respectively. The bias-corrected precipitation and temperature datasets are introduced into the SWAT model, calibrated and validated on the Tafna basin with good Nash criteria (NSEoutlet = 0.83). The discharge is over or under-estimated without bias-correction of RCM outputs, which highlights the necessity of applying bias-correction before using RCM outputs from Cordex-Africa for hydrological applications. The results show that the precipitation and discharge decreases, and temperature increases are more important with RCP 8.5 than with RCP 4.5, especially in the last decades of the 21st century.

Annual and seasonal mean tropical and subtropical precipitation bias in CMIP5 and CMIP6 models

Annual and seasonal mean tropical and subtropical precipitation biases in present-day climate simulations by phase 6 of the Coupled Model Intercomparison Project (CMIP6) models are evaluated. We analyze 23 CMIP6 models, which are grouped into those without (NOS) and with (SON) falling ice (snow) radiative effects (FIREs). The SON group is further divided according to treatments of ice-cloud radiative properties with separate (SON2) and combined cloud ice and snow contents (SON1). SON2 mitigates precipitation bias drastically relative to NOS, but SON1 overestimates precipitation in the subtropics, which deteriorates its performance relative to SON2 and NOS. Large offsets between SON2 and SON1 and between SON and NOS contribute to the slight improvement of CMIP6 relative to CMIP5, largely due to the contribution of SON2. The different treatments of ice-cloud radiative properties between SON2 and SON1 seem to be potentially linked to differences in precipitation biases. Results suggest that further in-depth investigation of cloud ice–radiation–circulation interactions is required.