This study examines the projected changes in the characteristics of the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) in terms of mean state, intensity and frequency, and associated rainfall anomalies over eastern Africa. Two regional climate models driven by the same four global climate models (GCMs) and the corresponding GCM simulations are used to investigate projected changes in teleconnection patterns and East African rainfall. The period 1976–2005 is taken as the reference for present climate and the far-future climate (2070–2099) under Representative Concentration Pathway 8.5 (RCP8.5) is analyzed for projected change. Analyses of projections based on GCMs indicate an El Niño-like (positive IOD-like) warming pattern over the tropical Pacific (Indian) Ocean. However, large uncertainties remain in the projected future changes in ENSO/IOD frequency and intensity with some GCMs show increase of ENSO/IOD frequency and intensity, and others a decrease or no/small change. Projected changes in mean rainfall over eastern Africa based on the GCM and RCM data indicate a decrease in rainfall over most parts of the region during JJAS and MAM seasons, and an increase in rainfall over equatorial and southern part of the region during OND, with the greatest changes in equatorial region. During ENSO and IOD years, important changes in the strength of the teleconnections are found. During JJAS, when ENSO is an important driver of rainfall variability over the region, both GCM and RCM projections show an enhanced La Niña-related rainfall anomaly compared to the present period. Although the long rains (MAM) have little association with ENSO in the reference period, both GCMs and RCMs project stronger ENSO teleconnections in the future. On the other hand, during the short rains (OND), a dipole future change in rainfall teleconnection associated with ENSO and IOD is found, with a stronger ENSO/IOD related rainfall anomaly over the eastern part of the domain, but a weaker ENSO/IOD signal over the southern part of the region. This signal is consistent and robust in all global and regional model simulations. The projected increase in OND rainfall over the eastern horn of Africa might be linked with the mean changes in SST over Indian and Pacific Ocean basins and the associated Walker circulations.
We analyze the observed (1950–2020) and expected (2021–2050) change in temperature extremes and heatwave characteristics over Europe across time, and the emergence of unfamiliar (Signal to Noise ratio, S/N > 1), uncommon (S/N > 2) and unknown (S/N > 3) conditions from the 'parents' generation (1961–1990) to the 'grandchildren' one (2021–2050). Children born in 1991–2020 experienced conditions of extreme heat that were different from those at the time of their parents: at the European scale, 28.5—42.1% of the population in 1991–2020 experienced unfamiliar (and 1.3% uncommon) occurrence of extreme temperatures (Tx90p). Large areas of Spain, France, Germany, Poland, the Baltic states and Ukraine experienced a significant increase in heatwave number, and, together with southern Italy and Scandinavia, enhanced cumulative heat (HWC, with large areas where the signal has emerged from variability since 2011, but, locally, earlier. People born in the next 30 years (2021–2050) are estimated to live in a climate with extreme heat substantially different from that of their grandparents. In some regions, nobody is projected to live in conditions that were 'familiar' in 1961–1990. Even under a moderately-low warming, 41.8–58.6% of the European population is expected to live in conditions of unfamiliar extreme temperatures, 28.5–41.5% uncommon, and 2.1–8.9% unknown, mostly over Spain (5.3–26.9%), the Alps (1.2–27.5%) and the Mediterranean region (1.7–15.8%). The change in heatwaves characteristics will not only concern their number, but also their length and intensity, with more than 60% of the European population projected to be exposed to unfamiliar HWC and up 16.4% to uncommon HWC.
Abstract In this study, we present simulations of a burned area at a European scale for the period 1990–2009 conducted with the Community Land Model (CLM). By using statistics on fire counts and mean fire suppression time from the European Fire Database, we refined the parameterization of the functions describing human ignition/suppression, and we modified the description of biomass availability for fires. The results obtained with the modified model show an improvement of the description of the spatial and interannual variability of the burned area: the model bias is reduced by 45%, and the explained variance is increased by about 9% compared to the original parameterization of the model. The observed relationships between burned area, climate (temperature and precipitation), and aboveground biomass are also reproduced more accurately by the modified model. This is particularly relevant for the applicability of the model to simulate future fire regimes under different climate conditions. However, results showed an overestimation of the burned area for some European countries (e.g., Spain and France) and an underestimation in years with an extreme fire season in Mediterranean countries. Our results highlight the need for refining the parameterization of human ignition/suppression and fuel availability for regional application of fire models implemented in land surface models.
In this work we present the results of the application of the consortium for small-scale modeling (COSMO) regional climate model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the coordinated regional climate downscaling experiment. An ensemble of climate change projections has been created by downscaling the simulations of four global climate models (GCM), namely: MPI-ESM-LR, HadGEM2-ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the downscaling. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa monsoon (WAM) area. By performing a singular spectrum analysis it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.
[1] In the paper “Assessment of future flood hazard in Europe using a large ensemble of bias-corrected regional climate simulations” by R. Rojas et al. (Journal of Geophysical Research, 117, D17109, doi:10.1029/2012JD017461, 2012), two author affiliations were erroneously reversed. The correct affiliations for all authors are provided here. R. Rojas,1 L. Feyen,1 A. Bianchi,2 and A. Dosio1 1Climate Risk Management Unit, Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy. 2Water Resources Unit, Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy.
The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of ~50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim; 1989–2008). Results are compared against a number of observational datasets. In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain. Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Niño (La Niña) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.
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