Unlike the global and regional assessment, the spatial-temporal variability of air temperature and precipitation, caused by climate change, must be more useful when the assessment is made at sub-regional to local scale. Thus, this study aims to assess the possible changes in pattern of air temperature and precipitation for late 21st century relative to present climate over Mozambique. For this purpose the downscaling to finer horizontal grid resolution of 50 km was run for present and under two Representative Concentration Pathways (RCPs), moderate RCP4.5 and strong RCP8.4, through a regional climate model RegCM4.0 driven by HadGEM2 global model. The climate pattern considered were analyzed for two time slices, baseline (1971–2000) and future (2070 − 2099) range within Mozambique borders. Over this study domain, the highest amounts of precipitation and the highest air temperatures are observed during summer season. However, the central region is quite warmer and rainier than the northern and southernmost regions. The regional model RegCM4.0 demonstrate agreement relative to observed weather stations and interpolated dataset from Climate Research Unit. The strong performance of RegCM4.0 is revealed by the more realistic local spatio-temporal climate features, tied to topography and geographical location of the study domain. The future increases in mean annual air temperature is well simulated by the model but, the spatial distribution and magnitude differ between the RCPs and over each of the three regions throughout the country. The sharp hottest response in the end of the century occurs in summer and spring seasons under RCP8.5, spatially over the central and northern region of the study domain, with some hot-spot in southern region. On the other hand, there is a predominance drier response in the annual mean precipitation but, during the summer season is observed a meridional dipolarization pattern, with wettest response over southernmost and drier response in northern and central regions of Mozambique.
Research Article
Evaluating possible changes in air temperature and precipitation pattern in Mozambique by comparing present and future RegCM4 simulation
https://doi.org/10.21203/rs.3.rs-1766346/v1
This work is licensed under a CC BY 4.0 License
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regional model
seasonal precipitation
future climate
The continued global emission of greenhouse gases (GHG) will lead to additional warming along with long-term changes in all components of the climate system, increasing the likelihood widespread and potentially irreversible in terrestrial system (Almazroui et al., 2021; Pachauri et al., 2014). With climate change running out of time to limit global warming to 1.5 ºC or to well below to 2 ºC above pre-industrial levels (Paris International Climate Agreement, 21st Conference of Parties), it is expected that the impact-related become more severe in developing countries mainly over those in sub-Saharan Africa (SSA). In recent decades, the air temperatures levels in SSA have been warming at a rate comparable to that of most other continents, and thus somewhat faster than global mean surface air temperature. Additionally, the warming is higher than it was in the past century, and therefore the continent became one of the most vulnerable regions to global climate change (Russo et al., 2016). In the upcoming decades for both global warming levels (GWLs) of increasing 1.5 and 2 ºC and under Representative Concentration Pathways (RCPs), air temperatures in Africa may rise sharply over most of African countries (Déqué et al., 2017; Lennard et al., 2018; Nikulin et al. 2018; State of the Climate in Africa, 2019). Along the extensive continent, there is observed a sharp geographical mean precipitation contrasts. Besides that, the Southern Africa is considered one of the few land-based regions in the world for which climate models are in agreement with precipitation (air temperature) decline (increase) as the global warming steps forward (State of the Climate in Africa, 2019; Pinto et al., 2020). Predictions of future climate change for Africa clearly suggest that the warming will continue and in average, the continent could be between 2 and 4 ºC warmer up to the end of this century. Meanwhile, over Southern Africa, there is a positive sign of change for temperature, with temperature rising faster at 2 ºC (1.5–2.5 ºC) as compared to 1.5 ºC (0.5–1.5 ºC) of global warming (Mavume et al., 2021). For instance, Dosio (2017) showed that under strongest radiative forcing scenario, warming of more than 3.5 ºC is projected in second quarter of the tropical summer season over most of the African continent. These statements are supported to those claimed in Intergovernmental Panel on Climate Change Assessment Reports (IPCC-ARs) (Hulme et al., 2001; Pachauri et al., 2014; Reay et al., 2007). Additionally, over the coastal and developing countries of Southern African region, such as Mozambique, there will likely experience increasing (decreasing) in frequency and intensity of heat waves (number of rainy days) and the spread of regions with extreme droughts, under future climate projections. Besides to these climate change signal most of Southern African countries have just experienced decreasing (increasing) in seasonal and annual mean precipitation (air temperature), and hence higher inter-annual variability (Déqué et al., 2017; Dosio, 2017; Maure et al., 2018; Pinto et al., 2020). Briefly, Africa is therefore a continent of high exposure and vulnerability, and within southern Africa, Mozambique is one of the “hotspots” for climate change-related impact. This situation is caused mainly by geographic location and high levels of poverty.
Studies focused on the evaluation of present and future climate change within Mozambique borders are scarce (e.g., Mavume et al., 2021). However, some regional climate modeling studies have been performed over extensive regions of the African continent, most of them considering generalized analysis for the Southern African region as a whole (e.g., Maure et al., 2018; Pinto, Jack, Hewitson, 2018). As stated by Steynor and Pasquini, (2019), if climate information’s are to be effective in building capacity to prepare for future climate change in Africa, understanding the local context of its impacts on African countries will be crucial to designing an effective adaptation framework. At small-scale, there is some local evidence of increasing mean air temperature and irregular distribution of precipitation within Mozambique borders, which partially support the global and regional results previously highlighted (INAM-Instituto Nacional de Meteorologia). However, it is known that the magnitude of climate change-related impact will not be homogeneous even on sub-regional scale. Whilst scores of global and regional studies for Africa has taken great strides forward, the ultimate goals of the sub-regional to local scale precipitation and air temperature variability that affects small countries such as Mozambique, remain little studied. Over Southern Africa, there are relatively few studies based on global climate models downscaled by regional climate models. For instance, in the last decades some studies have been using ensembles of high-resolution regional climate projections generated by Regional Climate Models (RCMs) within the Coordinated Regional climate Downscaling Experiment for Africa (CORDEX). In addition to the studies of Mavume et al. (2021), Queface (2009), as far as we know, there are no studies investigating the effects of climate change and responses to global warming under representative climate scenarios through a RCMs downscaled data at a sub-regional, countrywide and local scale in Mozambique. As stated in Mavume et al. (2021) the scarcity of dedicated studies on climate change projections at local levels in an undeniable fact.
The climate change concept is more useful in practice if the impact assessment is implemented at sub-regional and local scale. As claimed by several studies (e.g. Déqué et al., 2017; James and Washington, 2013; Lennard et al. 2018), the sub-regional and local air temperature warming is not determined only by the mean global warming. The spatial pattern of warming also strongly depends on region and season, as the regional air temperature response to global warming in Africa is very pronounced, although it is also strongly modulated by sub-regional and local scale processes. Local magnitude of warming can be much higher leading to more extreme and severe regional footprints of global warming than what could be expected from directly considering global warming (Nikulin et al., 2018). It is essential to understand the sub-regional climate response to global climate change over Mozambique as warming or drying rates across the country are likely to be faster or not than the regional and/or global average. Such as underlined by Steynor and Pasquini (2019), through a better understanding of climate change-related impact over local-scale, there is scope to design climate services that more readily fit the specific sub-regional context.
As stated by Hulme et al. (2001) for Africa context, but also true for Mozambique, the climates are both varied and varying: varied because they range from humid near equatorial regimes, through isolated seasonally-arid tropical regimes, to a narrow sub-tropical climate type, and varying because all these climates exhibit differing degrees of temporal variability. The geographical location of Mozambique make the country prone to frequent droughts and uneven precipitation distribution with two distinct seasons: a hot and wet summer season that extends from October to March, and another extremely dry and winter season from April to September. From this point of view, the trend line of observed weather stations indicate that the annual cycle of air temperature over this country of the tropical region is less contrasted than the precipitation. It is important to note that, this low annual air temperature amplitude results from a “harshless” winter season. Meanwhile there is a significant difference between dry and rainy season, consequently there is a greater amplitude of annual precipitation (Morishima and Akasaka, 2010). The spatial and temporal distribution of air temperature and precipitation pattern in Mozambique are combined in a similar annual and seasonal trend line response. However, the seasonal cycle of precipitation is not homogeneous over the country. There is observed delay or lag in onset of rainy season over central and northern regions relative to early onset in the southern region (Engelbrecht, Engelbrecht, 2016; Lumpkin, 2021; Monerie et al., 2019). Hence, the precipitation (air temperature) distribution and variability in this country is shaped as a remarkable northward-southward (eastward-westward) gradient. From the point of view of their connection with the seasonal trend and interannual variation during tropical summer season for example, it is observed a remarkable increasing (decreasing) trend of seasonal mean surface air temperature (annual mean precipitation) around the eastern coastal region of the Southern Africa. Means that, the annual mean precipitation (annual mean surface air temperature) has shown a decreasing (increased) trend across the whole region (Abiodun et al., 2020; Maure et al., 2018; Monerie et al., 2019; Morishima and Akasaka, 2010; Pinto, Jack and Hewitson, 2018; Tadross, Jack and Hewitson, 2005). Another point resulting from some research and worth highlighted is that, there is a well-pronounced hot semi-desert zone in present-day climatology over a restricted areas in southern region of Mozambique that is projected to expand around the region under projected global warming (Engelbrecht, 2016). The aforementioned changes may replace the current climate pattern with the consequences arising from.
The geographical location in the tropical region and along the coast strip of Southern Africa, besides to a rugged topography makes Mozambique a territory with single climate characteristics. However, in SSA countries there is not a place of great consensus among global or regional models which attempt to project the sign of precipitation change at the end of the 21st century, although as far as air temperature the results converge to an almost generalized warm-response (Déqué et al., 2017; Maure et al., 2018; Pinto, Jack and Hewitson, 2018). Therefore, to address the challenges of better and deepest understanding of the real impact of climate change in the small-scale through this country, Regional Climate Models (RCMs) became sophisticated and important tools used in the last decades to improve the coarse resolution of the Atmosphere-Ocean General Circulation Models (AOGCMs). Climate change projections of high quality are performed by downscaling techniques and are often required in climate change impact assessments studies at regional and local scales (Mavume et al., 2021). Furthermore, the dynamically downscaling of AOGCMs simulations not always improve the skills and make better performance to simulate the present and future climate. The regional models add values, especially allowing to resolve small-scale processes that are influenced by e.g. topographical details, coastlines and land-surface heterogeneities. When used for climate change projections, RCMs not only can introduce finer resolution to identify signals of local climate characteristics, but may also simulate some significant different regional and subregional climate patterns (e.g., Dosio, 2017; Giorgi et al., 2012; Otieno and Anyah, 2012; Sylla et al., 2010; Sylla, Giorgi and Stordal, 2012). Although these advantages, besides to the initial and boundary conditions, it is known that the output of numerical models can be strongly affected by the local characteristics of the study domain (Diffenbaugh et al., 2005; Giorgi and Mearns 1999; Koné et al., 2018; Maity, 2020; Mariotti et al., 2014; Önol, 2012; Ozturk et al., 2018; Steiner et al., 2009; Sylla, Giorgi and Stordal, 2012). In Africa, there are relative significant studies (e.g. Hulme et al., 2001; Monerie et al., 2019; Pinto et al., 2016, 2020; Tamoffo et al., 2019; Yuan et al., 2014) whose discussion has received permanent attention on global and regional scale. Few if any of these studies have focused on the Southern African region, and none have relied on the use of dynamic regional climate models to simulate future climate under representative pathways scenarios over Mozambique domain in detail. Thus, researches aimed evaluating the performance of the regional models and quantifying the simulated air temperature and precipitation changes in the 21st century within Mozambique boundaries are currently lacking.
As stated before, there has been relatively little studies published on future climate change scenarios for Mozambique. Unfortunately, the downscaling of AOGCMs outputs to finer horizontal grid spacing has received relatively little attention in Southern Africa region. Although the efforts from CORDEX and CMIP projects for Africa to fill these gaps, Creese and Washington (2016) argued that some ensemble models are not appropriated to model precipitation due to the divergences of climatology features across models. Thus, as claimed by Tamoffo et al. (2019), the downscaling using a common RCM is a plausible option to overcome this issue, in the case the RCM exhibits a good skill to reproduce the real climate. Therefore, the International Center for Theoretical Physics - Regional Climate Model (ICTP–RegCM) used here is one of the most reliable regional model to simulate climate of the tropical regions. Rather than considering the global warming levels (GWLs) 30-year mean surface air temperature increase 1.5 and 2 ºC ( e.g. Engelbrecht and Engelbrecht, 2016), here was used a single regional model driven by AOGCM under Representative Concentration Pathways (RCPs) scenarios. This was made through the fourth generation of ICTP-RegCM, hereafter RegCM4, to evaluate and quantify the present and future climate over Mozambique, through two experiments: moderate radiative forcing (RCP4.5) and strong radiative forcing (RCP8.5). The latter one is considered here as the most realistic business-as-usual scenario given the current trajectory of GHG emissions. Additionally, rather than simulations with different future time periods and fixed global background warming value with respect to pre-industrial level of mean air temperature, in the present studies were considered a single and traditional 2070–2099 relative to 1971–2000. One of the most pragmatic challenges to the prospect of long-term climate change-related impact for most of the Southern Africa countries is the wish to strengthen scientific basis on national-scale. Thus, for the first time to our knowledge is provided an analysis based on the response of air temperature and precipitation to climate change simulated through a regional model under two radiative forcing’s for Mozambique. Thus, considering these assumptions, we have investigated not only the seasonal climatology of the mentioned climate factors, but also, relates its signal with probability density function for both, present and future climate. Additionally, the simulations presented here were also focus on amplitude and seasonal characteristics of the abovementioned key climate variables at sub regional level. The paper is organized as follows: in section 2 it is made a brief description of the model structure and setup, details of observed and simulated dataset for Mozambique, the draft of simulation experiments and metrics used to assess model performance. Section 3 discusses the validation of the CRU data and model performance in simulating the reference climatology. The projected changes simulated by the RegCM4 model are then discussed in section 4, and in section 5 are outlined the summary and conclusions.
Model description (set up) and simulation design
The climate assessment for present and future climate is made through the analysis of air temperature and daily precipitation dataset over the study domain. The open source regional model, RegCM4 (available at ICTP - Regional Model, described in Giorgi et al., 2012) is used for the experiments in this study. For dynamical downscaling process the RegCM4 model was running driven by the AOGCM, HadGEM2 (Collins et al., 2008, 2011), with grid spacing of 50 km and the time spans conducted at three experiments divided into: historical run (1971–2005) (hereafter Ref), and two experiments runs for future period (2070–2099) forced by also two Representative Concentration Pathways (RCPs) scenarios, moderate radiative forcing (RCP4.5) and strong radiative forcing (RCP8.5) (Jubb, Canadell and Dix, 2013; Meinshausen et al., 2011; Moss et al., 2010; Van vuuren et al., 2011). Note that, Representative Concentration Pathways are a set of scenarios that represent pathways of radiative forcing based on socio-economic and technological development (Van Vuuren et al., 2011). The choice of RegCM4 simulation matrix applied here is far from being perfect, but the brief model setup description used to run the experiments, is based on the scheme combination mentioned in the most of literature as the better for tropical region. The experiments were carried out under the following parameterizations schemes: the radiation scheme of CCM3, modified version of the planetary boundary layer scheme of Holtslag et al. (1990), the resolvable scale precipitation scheme (SUBEX) to simulate large-scale precipitation, the mixed convection scheme parameterization of Emanuel (1991) and, the Biosphere-Atmosphere Transfer Scheme (BATS) to describe the land surface processes were applied (more description in Table 1).
The study domain is located in the southeastern of the African continent and is bounded by latitude 10º 27΄ S and 26º 57΄ S, and longitude 30º 91΄ E and 40º 51΄ E (Fig. 1), with three main types of Koppen-Geiger climate regions: temperate climate in most of the northern and portions of central region, semi-arid climate in little restricted areas of southern and central region and, tropical climate with rainy summer throughout the coast (Engelbrecht and Engelbrecht, 2016; Filho, Bassoi and De Faria, 2016). The country holds a long coastline bathed by the Indian Ocean with a total distance of about 2700 km.
The observed daily datasets in the study domain are scarce with large gaps in both spatial and temporal coverage, but there is some historical dataset available from the main weather station along the geographical boundaries of the country. For the model RegCM4 output performance evaluation and Climatic Research Unit reanalysis (University of East Anglia Climatic Research Unit - CRU, Harris et al., 2020) validation, were used daily observed weather station (ws) datasets available by the National Meteorological Agency - Mozambique (INAM-Instituto Nacional de Meteorologia). This data corresponds to the location points of the ws shown in Fig. 1, where each of the black marks corresponding to Pemba (PB), Nampula (NP), Lichinga (LC), Quelimane (QL), Tete (TT), Beira (BR), Chimoio (CH), Inhambane (IN), Xai-xai (XX) and Maputo (MP) WS. It is important to highlight that, the CRU dataset used here is updated by the inclusion of additional station observations, the interpolation process has been changed to use angular-distance weighting (ADW) and, the production of secondary variables has been revised to better suit this approach. Moreover, the implementation of ADW provides improved traceability between each gridded value and the input observations, allowing more informative diagnostics than the previous version (Harris et al., 2020). Another point worth highlighted is that, from the RegCM4 baseline simulation and CRU 0.5º x 0.5º grid space resolution data, were extracted the climatological mean (linear remapping) of air temperature and precipitation from the points around each corresponding latitude and longitude ws throughout the study domain. These extracted data made it possible comparative analyses of the present climate among the three datasets at local scale level. The analysis are performed for the all seasons (December-January-February, DJF; March-April-May; MAM; June-July-August, JJA; and September-October-November, SON), so that the entire year is considered. For the sub region, we also examine the annual cycle of both temperature and precipitation. In addition, the models are evaluated against weather stations and CRU dataset for the reference period, while RCP4.5 and RCP8.5 changes with respect to the Ref period of RegCM4 simulation over the seasons mentioned above.
Table 1. Model setup and physics process applied to run the simulations
To evaluate the regional model (RegCM4) performance to simulate precipitation and air temperature climatology in baseline period over the study domain, some statistical metrics were applied: the absolute bias (AB), mean absolute error (MAE), root mean square error (RMSE) and the pattern correlation coefficient (r) between each dataset. The performance metrics are all computed with respect to both the CRU and ws dataset to allow the validation of the CRU dataset and concurrency assessing the performance of RegCM4. However, it should be recognized that the performance metrics values should be considered as indicative of the model performance. Additionally to these metrics was applied the Probability Density Function (PDF) according to Eq. 1.
Where x is the climatological variable (temperature and precipitation), σ is the standard deviation and µ the mean of the dataset. To complement these statistical metrics the spatial time-average maps, are presented as annual and seasonal averages for the entire region domain and analyzed for each subregion.
Air temperature and precipitation annual trend-line
For the model to be used as a tool of faithful representation of changes in the future climate it is necessary to evaluate how the model captures the important climate features and simulate key variables such as precipitation and air temperature (air temperature and precipitation are two key indicators that characterize the state of the climate) within the boundaries of the study domain. It is worth remembering that, based on the clustering analysis of climatological similarity (not shown), the representative ws of each of the three climatological regions of the study domain was extracted (Fig. 2a and d from PB ws; Fig. 2b and e from QL ws; Fig. 2c and f from XX ws). Then, according with the probability density of daily temperature and precipitation, it is noted that the temporal distribution of these parameters is strongly seasonal. It is clearly observed the occurrence of high (low) air temperatures from October to March (April to September) and, in the same period is concentrated the most events with air temperature above 30 ºC. Additionally, these events are observed mostly in the central region (Fig. 2a-c). Meanwhile, the lower occurrence of daily precipitation is observed between May and September, and the lowest daily amounts (less than 20 mm per day) are recorded in northern and southern regions (Fig. 2d and f). On the other hand, from October to March the daily precipitation events above 20 mm are more usual, however, it is in the southern region where the smallest daily amounts of precipitation occur, compared to those recorded in central and northern region (Fig. 2d-f). One important point worth highlighting in the Fig. 2 is that, the further south, the greater the thermal and precipitation amplitude. This can be explained by the influence (absence) of the zonal flow linked to cold fronts during winter season (summer season), over the southern and part of central regions of the country. Hence, concerning to precipitation the inter-annual variability is higher in regions of lower seasonal precipitation amount, i.e, drier areas are also more variable year-to-year.
Unlike the probability distribution of the climate factors, the annual trend-line of mean air temperature and precipitation over the reference period (1971–2000) represented by observed ws, interpolated CRU and simulated RegCM4 datasets is shown in Fig. 3. There is a clear south to north increase in average precipitation amounts and a less well marked increase from interior to coastal areas. Driest provinces are those represented by MP, XX and TT ws. Globally, precipitation has similar averages between the ws in each region, but the rainy season is markedly more prominent over central (CH, BR and QL WS) and some areas in northern regions (PB, NP and LC WS) of Mozambique (Fig. 3a, b, c). At the same time, the lowest amount of precipitation are recorded over southern region (IN, XX and MP ws) of the study domain. Note that, despite the overstated seasonal amplitude and small lags in the annual trend-line, the regional model ReCM4 represents well both the annual cycle as well as the seasonal amplitude of precipitation in each ws throughout the study domain (Fig. 3a, b and c).
Such as observed for precipitation, the annual cycle and seasonal amplitude of air temperature are well represented by the three datasets, but with less differences in seasonal amplitude than those occurred for precipitation. The regional model RegCM4 and CRU data can capture the most stands outs mean air temperature in LC, CH and TT ws (Fig. 3d, e and f). It is important to point out this result because, the local topography and geographical location of these ws and surroundings (see Fig. 1) are the main climatic feature for the “outsider” annual air temperature trend-line. Thus, from the point of view of temporal distribution the RegCM4 and CRU datasets are fairly close to the annual cycle and amplitude of seasonal variation in air temperature and precipitation, relatively to observed ws (OBS) dataset (Fig. 4a and d).
To make clearer the results founded in the previous discussion and further assess the performances of the regional model and CRU reliability at grid point level, the mean bias was computed for CRU and RegCM with respect to ws datasets, as shown in Fig. 4. First of all, there is no generally applicable precipitation anomaly pattern (positive or negative bias) between OBS and CRU. However, in the OBS-RegCM and CRU-RegCM difference it is observed a negative predominant pattern year-round, means that there is a clear trend of the regional model overestimate OBS and CRU.
These wet bias are more sharped (≥ 50 mm/month) during the rainy season and over the northern region of the study domain (Fig. 4). Meanwhile, it can be observed an irregular response pattern in the air temperature anomaly, mainly for OBS-CRU and OBS-RegCM. Unlike that, RegCM slightly overestimates the CRU with emphasis on extended summer season. During the winter season, there is a small cold bias over most of southern region of the study domain (Fig. 4a). However, in absolute values, the anomaly between CRU and RegCM is less than 2 ºC. Finally, the large absolute biases simulated by RegCM are observed predominantly over the northern region of the study domain.
As known, the prerequisite for the credibility of RegCM4 simulations is the regional model systematic evaluation through comparisons against observations. The RegCM4 evaluation performance and CRU validation is based on how realistically they can reproduce the magnitude and spatio-temporal variability of both climate variables during each of single season throughout the study domain. For this purpose, were compared the mean monthly precipitation and air temperature from both datasets relative to baseline period of each ws shown in Fig. 1. Overall, there is strong consistency in amplitude of air temperature and its range of absolute distribution values, for RegCM4 and CRU datasets relative to OBS, although small differences in LC and TT ws are observed (Boxplot in Fig. 5). The Taylor diagrams summarize the spatio-temporal differences or similarities between simulated and observed datasets. As kindly explained by Tamoffo et al. (2019): the Taylor diagram displays three statistical measures with respect to one reference field. The distance between reference and individual points in the diagram corresponds to root-mean-square difference (rmse). The black-dashed radial lines display the pattern correlation (r) between the simulated and the reference field. The brown half-circles represent the spatial standard deviation (σ) between the simulated and the reference field.
The RegCM4 against OBS diagram shows a high r (≥ 95%) with low rmse and σ in almost perfect agreement with the reference (well below 0.5), providing an indication of strong consistency between the datasets. It means that, the datasets are more clustered and close to the reference field with best performances, following the real annual and seasonal air temperature patterns (Fig. 5, RegCM4 vs OBS). Similar strong consistencies are observed when RegCM is compared with CRU, but some points with r less than 95%. Except for the LC and TT ws, this result is even more supported by the lower bias indicated previously.
However, when compared precipitation dataset from RegCM4 and OBS, there are some significant differences in absolute values and its range of distribution. From the precipitation boxplot, the distribution between the three datasets lies in the similar ranges despite RegCM4 indicate large overlie dispersion (Fig. 6). The boxplot shows once again that, among the weather stations, TT and LC are those where the biggest difference is found (actually these ws overestimated a lot and fall out of the scale). As a consequence, it can be observed a shift of the range from median towards to 75th quartile of the box with a broader range values in the LC than TT and NP. Interestingly, the annual cycle and the dispersion of the three datasets are similarly represented (as can be seen by the presence of less extended upper whiskers and symmetrical boxes) in most of the central and southern regions. Meanwhile, although the great agreement in annual cycle (high correlation) of some ws, the Taylor diagram RegCM vs OBS can distinguish the biases associated with poor simulation of the annual cycle amplitude and those associated with the lag of seasonal pattern, indicated through the below and above the reference line respectively (Fig. 6, both Taylor diagram).
One point worth to highlight is that the uncertainties associated to precipitation are expected because the great spatial and seasonal variability, and its dependence to local characteristics. Furthermore, the regional model generates its own internal climate variability within the nested domain, which may differ from that of the driving GCM over the same region and lead to some different projections. As pointed out before, topography, coastlines, land surface heterogeneity and smaller scale physical process are sometimes well accounted by regional models as opposed to GCM (Nikiema et al. 2017). Although the crucial issue to simulating precipitation magnitude, it can be claimed that RegCM4 runs and CRU dataset reproduce well basic patterns, supporting the seasonal cycle and depicted wetter character of SON-DJF relative to the rest of the year. These features are aligned with the observed ws boxplots. Hence, both datasets faithfully represent the ws data in situ.
The spatial distribution of CRU and the difference relative to RegCM4 for seasonal mean air temperature and precipitation is shown in Fig. 7 and Fig. 8. In general, the CRU dataset represents well the spatial features of the climatological pattern of air temperature and precipitation reproducing with good levels of seasonal agreement. It is important to emphasize some notable features associated to the magnitude of both climate factors.
As discussed in the previous section, the annual and seasonal cycle of average air temperature and precipitation follow the same pattern over the study domain, so that a very dry period and slightly low temperatures occurs from middle-late autumn to the late spring of the south hemisphere (Fig. 7b, c and d). The lowest temperatures recorded (less than 15 ºC) during the annual cycle occur in the higher altitude regions, particularly in the north of TT and QL, and west side of LC and CH ws respectively (Fig. 7c). On the other hand the higher temperatures (around 30ºC or little over) occurring during summer season are recorded mostly throughout north and central coastal region of the country (specifically surrounding NP, QL and BR) although, isolated surrounding areas of TT and XX ws also register similar levels of air temperature (Fig. 7a-d). It is important to note that, the air temperature pattern year-round is distributed zonally decreasing from the coast to the interior of the continent (Fig. 7a-d). Because of the well-defined differences in the climatological pattern between the hottest and coldest seasons in Mozambique, it is clearly observed that, the spatial difference between DJF and JJA temperature (absolute value between + 10 to + 12 ºC) for each region is greater than, between MAM and SON (absolute value around + 2 ºC). This observation is an indicative of greater temporal variability in air temperature from summer to winter seasons (Fig. 7a and c) and, lower between transition seasons (Fig. 7b and d).
Meanwhile, for the most of the year, RegCM4 underestimates CRU dataset, choiring with the air temperature annual trend illustrated previously (Fig. 7e-h). The mentioned exception occurs in the SON quarter, with a “tripolarization” of anomaly being negative (positive) over most of the northern (central) and southern regions of the study domain. In addition, a positive anomaly hotspot is observed in all quarters over northwest LC ws areas. Note also that, the greater negative anomaly is observed over the regions of higher air temperatures records. The range of variation of the air temperature anomaly leads to the idea that the regional model RegCM4 is so closed to CRU dataset.
As far as precipitation is concerned, the spatial distribution of mean seasonal captured by CRU and the relative difference from RegCM4 is showed in Fig. 8. The spatio-temporal pattern of precipitation across the study domain replicate two important tropical climate characteristics: the first one is that there is a great seasonal variability of precipitation with a rainy summer season (Fig. 8a and b) and a dry season (Fig. 8c and d); second, the highest amount of precipitation occurs predominantly over the central region and the lowest in the southern region (Fig. 8a-d). From this point of view, the CRU datasets estimate well the main seasonal distribution and meridional trend of precipitation. In the meantime can also worth highlight that, when it is observed the extended austral summer season easily can be noted the higher precipitation amount occurring during DJF to MAM (Fig. 8a-b) and lower from JJA to SON (Fig. 8c-d). Against this backdrop it is easily observed that the magnitude of simulated seasonal precipitation by the RegCM4, is predominantly lower than CRU (Fig. 8e-h). This observation is particularly true during DJF and MAM, leading to idea according with there is a trend to RegCM4 underestimate relative to CRU dataset. However, during the DJF and MAM quarters is observed some overestimates mainly over southernmost region and some restricted areas of central and northernmost regions (Fig. 8e and f). In addition, there is also observed small-overestimates over central region during JJA and SON (Fig. 8g and h).
It is important to note that, the most important consequence of the inter-annual shifting of the Intertropical Convergence Zone (ITCZ) is the annual exchange of wet and dry seasons in tropical Africa. To some extent, precipitation seasonality over the northern and central regions of the study domain is explained by the intensification (weakening) in summer (spring) season of the ITCZ and Congo Air Mass (CAM). Meanwhile, the advancement of baroclinic systems associated with the sudden convective storms over the southern region has also contributed to seasonal variability of mean precipitation. The Mascarenhas subtropical high system, predominant synoptic system over the Indian Ocean, Santa Helena subtropical high system over Atlantic and, the Tropical Temperate Trough strongly control the seasonal evolution of precipitation, mainly over the southern and central regions of Mozambique. More details about the most important synoptic systems in southern Africa can be found in several literature (e.g. Cook, 2000; Eckardt et al., 2013; Todd; Washington; Palmer, 2004; Vigaud et al., 2009; Vigaud; Pohl; Crétat, 2012; Yuan et al., 2014).
The projected climate changes discussion begins with comparison of spatial features of future period relative to the baseline for both climate parameters considered in the present study. The mean changes in annual and seasonal simulated air temperature for future climate (2070–2099) under RCP4.5 and RCP8.5 scenarios, with respect to reference period (1971–2000), were made by averaged in time resulting in sub-regional maps over the study domain (Fig. 9). Despite the apparent similarity among the quarters, the future warming is not uniform and spreads in space (sub-regionally) and time (seasonally) within the borders of Mozambique. Differences in spatial pattern of mean annual temperature between both scenarios relative to reference period are large, being more significant under RCP8.5 (4–5.5ºC) than RCP4.5 (around 3 ºC) scenarios, suggesting as expected, higher increase for the strongest radiactive forcing (Fig. 9a and f). A comparison between RCP4.5 and RCP8.5 (not shown) can confirm this overwhelming air temperature increase under the worst radiative forcing scenario. Regionally, these increases are particularly more pronounced (≥ 5ºC) over most of central region and western areas of XX weather stations, probably as a result of intensification of hot air mass coming from central region of southern Africa, and the extent of the driest areas on the interior of Gaza province. This statement is supported in Howard and Washington (2019, 2020), who discussed in detail about the predominance of the known Congo Air Mass over the northern part of southern Africa region. Although the general warming response, over the southern and northern region of Mozambique the signal is relatively smaller (1.5 to 2.5 ºC for RCP4.5 and 2.5 to 4 ºC for RCP8.5). Meanwhile, there is observed a hotspot region embedded in northward of XX weather station with higher warming signal (+ 3.0 ºC for RCP4.5 and + 4.5 ºC for RCP8.5). Similarly, there is an increase in mean seasonal air temperature pattern in both scenarios but higher in RCP8.5 (between 4 and 6 ºC), which will result in greater warming in the latter (Fig. 9g-j). The most outstanding seasonal warming occurs in the SON-DJF for both scenarios (3 to 4 ºC for RCP4.5 and, 4.5 to 6 ºC for RCP8.5) and, at sub-regional level concentrated over central region of Mozambique (Fig. 9b, e, g and j). This result is similar to that showed in the “Ministry of foreign affairs of the netherlands (2018)” report, according to which the highest increases, up to 3.0°C increase by 2055, are expected for inland and southern regions (northwest of XX weather station), especially the Limpopo and Zambezi valleys. Meanwhile, for both scenarios there is a spatial extent of warming (+ 4 ºC) in the narrow region over most surrounding XX weather station. It is important to point out that, there is a trend of extension of semi-arid area around the XX associated with the expansion of the Namibian desert (Eckardt et al., 2013; Nikiema et al., 2017). Likely it can be the main reason of aforementioned hotspot warming. Seasonally, the lowest increasing signal (around 3 ºC for RCP4.5 and 4.5 ºC for RCP8.5) in mean air temperature is observed during MAM and JJA (Fig. 9c, d, h and i). It is also important to stress that, during the extended summer season (SON- DJF) the smallest warm response (less than 2.5 and 3.5ºC for RCP4.5 and RCP8.5, respectively) is observed over the northern region mainly along the coast (Fig. 9b, e, g and j). This observation converge with one of the results from Nikulin et al. (2018) and Mavume et al. (2021). The authors of these papers indicate that the highest increase in annual mean air temperature was found over the subtropics and the smallest one over many coastal regions. A possible explanation for the abovementioned lower warming response throughout the coastal regions may be found in the predominant transport of low-level ocean moisture through the zonal wind flow which leads to increase of relative humidity amount. Additional physical processes are also explained by Cook, (2000), Eckardt et al. (2013), Sylla, Giorgi and Stordal (2012). As previously noted, under increases of warming, the projected changes in the magnitude of air temperature are significantly weaker for RCP4.5 when compared to strongest radiative forcing, RCP8.5 (Almazroui et al., 2021; Pinto et al., 2020; Mavume et al., 2021).
The results found here indicate that, throughout the last third of 21st century, a large increase in air temperature is likely over most of the central region and a contiguous hotspot in southern region of Mozambique. Meanwhile, a relatively smaller but still significant increase is projected to northern and southernmost regions of the country, particularly under RCP8.5. Unfortunately as highlighted previously, there are few studies of regional simulations that have made a particular discussion on local long-term scale within the Mozambique borders. Nonetheless, the results found here are consistent with some of earlier studies, although the difference on spatial domain analysis: for example, Russo et al. (2016), Dosio (2017), Nikulin et al. (2018), Maure et al. (2018) and Mavume et al. (2021) projected future changes with an ensemble of CORDEX Regional Climate Models, Almazroui et al. (2020) using CMIP6 under Shared Socioeconomic Pathways to project future climate and, Pinto et al. (2020) testing a Geoengineering Large Ensemble to project the air temperature response to increases in Solar Radiation Response.
As the future climate can be indicated by both, the shift in the mean and the change in the variability, it is important to investigate these changes under future scenarios relative to present climate. The seasonal mean air temperature changes illustrated in the previous figures, can be analyzed from the point of view of maximum and minimum air temperature distribution density in both radiative forcing (RCP4.5 and RCP8.5) against reference period, at the level of each subregion (north, central and south) of the study domain. For this purpose the probability distribution function (PDF) is used here to illustrate the future climate feature shifts relative to baseline (Fig. 10). In the present study, PDFs are calculated simply as normalized frequency histograms by applying its general form, as illustrated in Kay (2012). The regional averaged of maximum (minimum) air temperature for the reference period (vertical shaded band) range from 20 to 28.5 ºC (18 to 21.6 ºC), with the highest averaged identified in the central region of Mozambique (red line tipping point most shifted to the right, Fig. 10a and b). Similar results were found in the previous discussion. The modifications of the PDF are clearly illustrated by the displacement of the each density function to the right-side relative to reference period. It means that, there is a gradual trend of increasing maximum and minimum daily air temperature in both future scenarios, however, greater increase is observed for RCP8.5 (broken-dash line) than for RCP4.5 (dash-dot line) as expected. Over the northern (black line), central (red line) and southern (green line) regions, the variations of regional mean maximum air temperature are 4.4, 4.9 and 4.9 ºC respectively, but in absolute mean the greater value is observed over the central region of the study domain (Fig. 10a). For RCP4.5 radiative forcing these variations are slightly smaller, between 2.5 to 3 ºC. In the same way as observed for maximum air temperature, the PDF curves are displaced to right side relative to reference period and, a slight decrease in the tipping point from reference (solid lines) to both future scenarios (dash-dot and broken-dash lines), except for southern region (green lines). On the other hand, the variation of regional mean minimum air temperature under RCP8.5 (RCP4.5) are 3.8 ºC (2.2 ºC), 4.2 ºC (2.5 ºC) and 4.4 ºC (2.5 ºC) over the northern, central and southern regions respectively. The increase in standard deviation (reduction in PDF curve amplitude) and regional mean (expansion of the area below the PDF curves) are an indication of increase in amplitude and frequency of maximum and minimum daily air temperatures, as the radiactive forcing increases throughout the study domain (Fig. 10a and b).
Future changes in annual and seasonal mean precipitation are displayed in Fig. 11. From the difference between RCP4.5 and RCP8.5 radiative forcing’s relative to reference period, there is a generalized predominance of drier response in annual mean precipitation throughout the study domain. Under the point of view of annual time scale, the greatest reduction (between − 40 and − 60mm for RCP4.5, and more than − 90mm for RCP8.5) is observed in narrow areas over the northern region and surrounding QL ws for both scenarios but, sharper under RCP8.5 (Fig. 11a and f). On the other hand, the lowest reductions in mean annual precipitation is estimated over some areas around TT ws and almost entire south regions (Fig. 11a and f). On the seasonal precipitation responses, there is a dipolar distribution in summer season (DJF), with drier response over north-central and wettest, over some areas in central and most of southern regions. Additionally, during DJF months it is observed a drier response (between − 50 and − 80 mm for RCP4.5, and more than − 80mm for RCP8.5) over the northern and a narrow strip in the central regions of the study domain. At the opposite pole it is observed the wettest response (+ 100 mm for RCP8.5) over the southern region (Fig. 11b and g). Meanwhile, under RCP4.5 the difference with respect to reference period is very small (less than 20 mm). This heterogeneous precipitation pattern in DJF is unique among the seasons analyzed here. It means that, the magnitude of projected precipitation in DJF under RCP8.5 is overwhelmingly higher than reference period and RCP4.5 radiative forcing, for far future (2070–2099) in Mozambique.
Similar findings was claimed in Programme (2017) but for precipitation trends analysis during present climate (1981–2017). In this study was stated that, trends in seasonal precipitation appear positive (increasing) in the southernmost third and western areas of the country and moderately decreasing in the northern provinces. In the same climate report is stated that, the variation of precipitation trends along the season indicates that the increase in seasonal precipitation in the south and west of the country mostly arises from increases in December and January, already the wettest period of the season. Additionally, the decrease in seasonal rainfall in the central and north of the country are mostly due to decreases in early season rainfall. Likewise, projected changes in mean precipitation for 2071–2100 relative to 1961–1990 assessed in Mavume et al. (2021), showed similar results in the annual time scale but small differences come up on the seasonal time scale relative to the results presented here. For instance, Mavume et al. (2021) stated that all RCP scenarios point to above average precipitation in much of Mozambique during the DJF season, regardless of the period. This statement is far from the bipolar pattern of seasonal precipitation presented here.
Unlike the bipolarization projected to summer season, during the transition quarters (MAM, JJA and SON), the spatial response is similar throughout the country with the drier response being predominant but more sharply in the spring (Fig. 11e and j) than autumn (Fig. 11c and h). On the other hand, it is observed that there is very small change in precipitation (small drier response) during the winter season (Fig. 11d and i) indicating that, the future seasonal precipitation will be closed to winter pattern as the reference period. Another point worth highlighted from the results presented here is that, the drier response in annual mean over southern region of Mozambique is mainly resulting from drier response during spring season, while over the northern-central is due to drier signal in both, spring as well as summer season. The north–south bipolar projected changes during summer-season precipitation are in agreement with some previous projections made for southern Africa region, including Mozambique sub-regions. For instance, Almazroui et al. (2020) found similar bipolarization of summer precipitation using global CMIP6 models under strong Share Socioeconomic Pathways but, with some little differences in spatial distribution over northern region. Besides this study, Maure et al. (2018) emphasizes the bipolar response of summer precipitation but, unlike the present results, the projected precipitation changes in the latter study also indicates small differences in spatial pattern of annual mean, showing a slight overestimation over the northern region of Mozambique. Corroborating with these results, Pinto et al. (2016, 2020) and Dosio et al. (2020) stated that, relative to historical period there is projected significant decreases in total annual mean precipitation over southern Africa up to the end of 21st century, under climate scenarios considered in each of these studies.
Although the wet response in summer season over southern region of Mozambique, mainly under RCP8.5 radiative forcing (showed in Fig. 11), the dryer response year-round is overwhelmingly uppermost throughout the study domain. This general result can be seen by PDF curves for regional maximum and average precipitation distribution shown in Fig. 12a and b respectively. The regional mean of maximum (average) precipitation under reference period ranges from 23.7 to 24.7 mm/day (3.7 to 5.3 mm/day), represented by vertical shaded band (Fig. 12a and b). The tipping points for both maximum and average precipitation over each of the three regions are shifted to the left side relative to the reference period, means that, there is a generalized drier response throughout the study domain. But the most significant effect of this negative trend is observed over central (red lines) and northern (black lines) regions of Mozambique, under RCP8.5 than RCP4.5.
This result can be interpreted as higher standard deviation (smaller amplitude of the PDF curves) and higher average, hence, greater dispersion of rainfall compared to regional average. Otherwise, the distribution of regional precipitation (maximum and averaged) is more closed to the reference over the southern region (green line), under RCP4.5 (Fig. 12a and b). Although the distribution density curves between both future scenarios are similar (dash-dot line for RCP4.5 and broken-dash line for RCP8.5), the most significant negative effect occurs with RCP8.5 radiative forcing. The present results indicate that the effect of projected changes in precipitation are enhanced for the high-emission scenario, being more significant during austral summer season and, over the central and northern regions of Mozambique. It is also important to point out that small variations in the absolute amount of precipitation in several short time ranges can lead to significant changes in the climate pattern over the country.
In the present study, we investigated the performance of regional model RegCM4 in reproducing present climatology and assess the simulated future changes under two Representative Concentration Pathways, RCP4.5 and RCP8.5 radiative forcing, over Mozambique. As shown here, the country has a heterogeneous and complex climatic feature resulting from large-scale flow interactions and local processes related to topography and land-sea contrasts. To date, there are few regional and sub-regional modeling studies that have been limited the discussion within the boundaries of the present study domain. The focus of the present analysis were performed on spatial and temporal pattern and variability for air temperature and precipitation.
In the validation of simulated present climate, the regional model RegCM4 performs quite well the air temperature and precipitation climatology in particular picking up its annual trend and spatio-temporal variability year-round, over the study domain. However, although the model show a similar performance when compared to observed climatological weather stations and interpolated CRU datasets, there are biases found for both parameters in different magnitudes. The most significant biases of the regional model were observed for the amplitude and seasonal magnitude of precipitation climatology, mainly during summer season. The precipitation uncertainties are more pronounced than temperature, because this parameter is quite variable in space and time. Moreover, another important reason for these uncertainties is that, precipitation is very affected by the local physical processes and how the internal model processes are described in the models. Although the air temperature overestimates and precipitation underestimate in most of the seasons, the RegCM4 generate higher correlation coefficients relative to weather station and CRU datasets.
Mozambique has been identified as one of the most prominent climate change hotspots in southern Africa region. The regional studies have shown that, the most significant global warming impacts are related to hottest response in almost all countries in southern Africa region, including Mozambique (e.g. Almazroui et al., 2020; Maure et al., 2018; Pinto, Jack and Hewitson, 2018). In the present research, were found out indications of increase in amplitude and frequency of maximum and minimum daily temperatures for the last third of the 21st century, under both radiative forcing’s (RCP4.5 and RCP8.5) throughout the study domain. In addition, this increase is more pronounced under RCP8.5 and over the central region of Mozambique. This sub-regional to local scale result was showed in “Ministry of foreign affairs of the netherlands (2018) report. It is important to note that, there was observed similar finding in previous studies ( e.g, Mavume et al., 2021; Queface, 2009) according to which, the modulation of coastal regions air temperatures by oceans leads to warming considerably less than the inland regions. Otherwise, regarding to precipitation the annual mean for far future (2070–2099) shows a drier response throughout the country, particularly over northern and restricted areas of the central region. However, the spatial distribution of precipitation changes in the summer season, reveals a meridional dipole-like pattern mainly under RCP8.5 scenario with drier response over north-center and wettest response over southern region of the country. Even if there is no clear and significant trend in seasonal precipitation for some restricted regions, such as central region of Mozambique (with emphasis to surrounding areas of BR, CH and TT weather stations), the hottest response overwhelming through the entire domain can increase aridity and decrease runoff through the intensification of regional hydrological cycle. This Statement is also supported by Alfaro-Córdoba, Hidalgo and Alfaro (2020), and Almazroui et al. (2021). Therefore, negative effects may occur in the river basins and adjacent agricultural areas in the central region.
This study is perhaps the first one using regional model RegCM4 downscaled outputs under Representative Concentration Pathways to assess climate projections over Mozambique, opening a local-scale window on climate-related impact. As a summary assessment, the present study appears to provide a good representation of the most important features of the present and future climate of Mozambique, although some systematic biases persist. Meantime, our results support the conclusion that climate change projections based on regional model, adds physically consistent spatial details over sub-regional to local scale which needs to be properly characterized. In fact the regional models performance are affected by the boundary forcing’s, but the internal model physics is more important to sub-regional processes affected by topography, land use and coastline features. Thus, clearly, more studies are needed to rely on the careful evaluation and compounded information deriving from multiple sources, reproducing in this way the present and projected the future climate within Mozambique boundaries.
Conflicts of interest/Competing interests: Not applicable
Availability of data and material and Code availability: Not applicable
Consent for publication: Not applicable
Ethics approval/declarations: Not applicable
Funding: This research was funded by the Conselho Nacional de Desenvolvimento Científico (CNPq).
Authors' contributions: Telmo Cosme António Sumila wrote the main manuscript text and prepared figures. Angelica Durigon and Simone E.T Ferraz reviewed the main manuscript and figures. All authors read and approved the final manuscript.
Acknowledgements: This research was supported by Conselho Nacional de Desenvolvimento Científico (CNPq), Grupo de Pesquisas em Clima-Universidade Federal de Santa Maria (GPC-UFSM), and Laboratório de Agrometeorologia Aplicada-UFSM (AgroLab-UFSM). Deep thankful to all who directly or indirectly contributed to this research.
- Abiodun, B. J. et al. Potential impacts of specific global warming levels on extreme rainfall events over southern Africa in CORDEX and NEX-GDDP ensembles. International Journal of Climatology, v. 40, n. 6, p. 3118–3141, 2020.
- Alfaro-Córdoba, M.; Hidalgo, H. G.; Alfaro, E. J. Aridity trends in central america: A spatial correlation analysis. Atmosphere, v. 11, n. 4, 2020.
- Almazroui, M. et al. Projected Change in Temperature and Precipitation Over Africa from CMIP6. Earth Systems and Environment, v. 4, n. 3, p. 455–475, 2020.
- Almazroui, M. et al. Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs. Earth Systems and Environment, v. 5, n. 1, p. 1–24, 2021.
- Collins, W. J. et al. Evaluation of HadGEM2 model. Meteorological Office Hadley Centre, Technical Note 74, p. 47 pp., 2008.
- Collins, W. J. et al. Development and evaluation of an Earth-System model - HadGEM2. Geoscientific Model Development, v. 4, n. 4, p. 1051–1075, 2011.
- Cook, K. H. The South Indian convergence zone and interannual rainfall variability over Southern Africa. Journal of Climate, v. 13, n. 21, p. 3789–3804, 2000.
- Creese, A.; Washington, R. Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble. Journal of Geophysical Research, v. 121, n. 22, p. 13,415-13,442, 2016.
- Déqué, M. et al. A multi-model climate response over tropical Africa at +2 °C. Climate Services, v. 7, p. 87–95, 2017.
- Diffenbaugh, N. S. et al. Fine-scale processes regulate the response of extreme events to global climate change. Proceedings of the National Academy of Sciences of the United States of America, v. 102, n. 44, p. 15774–15778, 2005.
- Dosio, A. Projection of temperature and heat waves for Africa with an ensemble of CORDEX Regional Climate Models. Climate Dynamics, v. 49, n. 1–2, p. 493–519, 2017.
- Dosio, A. et al. A tale of two futures: Contrasting scenarios of future precipitation for West Africa from an ensemble of regional climate models. Environmental Research Letters, v. 15, n. 6, 2020.
- Eckardt, F. D. et al. The nature of moisture at Gobabeb, in the central Namib Desert. Journal of Arid Environments, v. 93, n. January 2012, p. 7–19, 2013.
- Engelbrecht, C. J.; ENGELBRECHT, F. A. Shifts in Köppen-Geiger climate zones over southern Africa in relation to key global temperature goals. Theoretical and Applied Climatology, v. 123, n. 1–2, p. 247–261, 2016.
- Filho, L. C. De A. L.; Bassoi, L. H.; De Faria, M. A. Variabilidade espacial e estabilidade temporal do armazenamento de água em solo arenoso cultivado com videiras irrigadas. Irriga, v. 1, n. 1SpecialIssueSpecialEdition-GreatCultures, p. 319–340, 2016.
- Giorgi, F. et al. RegCM4: Model description and preliminary tests over multiple CORDEX domains. Climate Research, v. 52, n. 1, p. 7–29, 2012.
- Giorgi, F.; Mearns, L. O. Introduction to special section: Regional climate modeling revisited. Journal of Geophysical Research: Atmospheres, v. 104, n. D6, p. 6335–6352, 1999.
- Harris, I. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, v. 7, n. 1, p. 1–18, 2020.
- Howard, E.; Washington, R. Drylines in southern Africa: Rediscovering the Congo air boundary. Journal of Climate, v. 32, n. 23, p. 8223–8242, 2019.
- Howard, E.; Washington, R. Tracing Future Spring and Summer Drying in Southern Africa to Tropical Lows and the Congo Air Boundary. Journal of Climate, v. 33, n. 14, p. 6205–6228, 2020.
- Hulme, M. et al. African climate change: 1900-2100. Climate Research, v. 17, n. 2 SPECIAL 8, p. 145–168, 2001.
- James, R.; Washington, R. Changes in African temperature and precipitation associated with degrees of global warming. Climatic Change, v. 117, n. 4, p. 859–872, 2013.
- Jubb, A. I.; Canadell, P.; Dix, M. Representative Concentration Pathways ( RCPs ). Australian climate change science program, p. 5–7, 2013.
- Kay, A. Introduction and Review of Statistics. Operational Amplifier Noise, p. 1–11, 2012.
- Koné, B. et al. Sensitivity study of the regional climate model RegCM4 to different convective schemes over West Africa. Earth System Dynamics, v. 9, n. 4, p. 1261–1278, 2018.
- Lennard, C. J. et al. On the need for regional climate information over Africa under varying levels of global warming. Environmental Research Letters, v. 13, n. 6, 2018.
- Lumpkin, R. State of the climate in 2020 Special Supplement.WMO report. v. 97, n. August, 2021.
- Maity, S. Comparative assessment of two RegCM versions in simulating Indian Summer Monsoon. Journal of Earth System Science, v. 129, n. 1, 2020.
- Mariotti, L. et al. Seasonal and intraseasonal changes of African monsoon climates in 21st century CORDEX projections. Climatic Change, v. 125, n. 1, p. 53–65, 2014.
- Maure, G. et al. The southern African climate under 1.5 °c and 2 °c of global warming as simulated by CORDEX regional climate models. Environmental Research Letters, v. 13, n. 6, 2018.
- Mavume, A. F. et al. Analysis of climate change projections for mozambique under the representative concentration pathways. Atmosphere, v. 12, n. 5, 2021.
- Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, v. 109, n. 1, p. 213–241, 2011.
- Ministry of foreign affairs of the netherlands. Climate Change Profile Mozambique. Ministry of Foreign Affairs of the Netherlands, p. 20, 2018.
- Monerie, P. A. et al. Predicting the seasonal evolution of southern African summer precipitation in the DePreSys3 prediction system. Climate Dynamics, v. 52, n. 11, p. 6491–6510, 2019.
- Morishima, W.; Akasaka, I. Seasonal Trends of Rainfall and Surface Temperature Over Southern Africa. African Study Monographs, v. 40, n. March, p. 67–76, 2010.
- Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature, v. 463, n. 7282, p. 747–756, 2010.
- Nikiema, P. M. et al. Multi-model CMIP5 and CORDEX simulations of historical summer temperature and precipitation variabilities over West Africa. International Journal of Climatology, v. 37, n. 5, p. 2438–2450, 2017.
- Nikulin, G. et al. The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble. Environmental Research Letters, v. 13, n. 6, 2018.
- Önol, B. Effects of coastal topography on climate: High-resolution simulation with a regional climate model. Climate Research, v. 52, n. 1, p. 159–174, 2012.
- Otieno, V. O.; ANYAH, R. O. Effects of land use changes on climate in the Greater Horn of Africa. Climate Research, v. 52, n. 1, p. 77–95, 2012.
- Ozturk, T. et al. Future projections of temperature and precipitation climatology for CORDEX-MENA domain using RegCM4.4. Atmospheric Research, v. 206, p. 87–107, 2018.
- IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of working group I, II, III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team Pachauri, R. K. et al.] IPCC, Geneva, Switzerland, 151 pp.
- Pinto, I. et al. Evaluation and projections of extreme precipitation over southern Africa from two CORDEX models. Climatic Change, v. 135, n. 3–4, p. 655–668, 2016.
- Pinto, I. et al. Africa’s Climate Response to Solar Radiation Management With Stratospheric Aerosol. Geophysical Research Letters, v. 47, n. 2, 2020.
- Pinto, I.; JACK, C.; Hewitson, B. Process-based model evaluation and projections over southern Africa from Coordinated Regional Climate Downscaling Experiment and Coupled Model Intercomparison Project Phase 5 models. International Journal of Climatology, v. 38, n. 11, p. 4251–4261, 2018.
- Programme, W. F. Mozambique: A Climate Analysis. 2017.
- Queface et al., A. Historical overview of natural disasters. Climate Change Report: Study on the Impact of Climate Change on Disaster Risk in Mozambique, INGC n. June, 2009.
- Reay, D. et al: Climate Change 2007: Syntesis Report. Intergovernmental Panel on Climate Change, 2007
- Russo, S. et al. When will unusual heat waves become normal in a warming Africa? Environmental Research Letters, v. 11, n. 5, 2016.
- World Meteorological Organization. State of the Climate in Africa. WMO-No. 1275, 2020.
- Steiner, A. L. et al. Land surface coupling in regional climate simulations of the West African monsoon. Climate Dynamics, v. 33, n. 6, p. 869–892, 2009.
- Steynor, A.; Pasquini, L. Informing climate services in Africa through climate change risk perceptions. Climate Services, v. 15, n. November 2018, p. 100112, 2019.
- Sylla, M. B.; Giorgi, F.; Stordal, F. Large-scale origins of rainfall and temperature bias in high-resolution simulations over southern Africa. Climate Research, v. 52, n. 1, p. 193–211, 2012.
- Tadross, M.; Jack, C.; Hewitson, B. On RCM-based projections of change in southern African summer climate. Geophysical Research Letters, v. 32, n. 23, p. 1–4, 2005.
- Tamoffo, A. T. et al. Process-oriented assessment of RCA4 regional climate model projections over the Congo Basin under 1.5∘C and 2∘C global warming levels: influence of regional moisture fluxes. Climate Dynamics, v. 53, n. 3–4, p. 1911–1935, 2019.
- Todd, M. C.; Washington, R.; Palmer, P. I. Water vapour transport associated with tropical-temperate trough systems over southern Africa and the southwest Indian Ocean. International Journal of Climatology, v. 24, n. 5, p. 555–568, 2004.
- Van Vuuren, D. P. et al. The representative concentration pathways: An overview. Climatic Change, v. 109, n. 1, p. 5–31, 2011.
- Vigaud, N. et al. Moisture transport between the South Atlantic Ocean and southern Africa: Relationships with summer rainfall and associated dynamics. Climate Dynamics, v. 32, n. 1, p. 113–123, 2009.
- Vigaud, N.; Pohl, B.; Crétat, J. Tropical-temperate interactions over southern Africa simulated by a regional climate model. Climate Dynamics, v. 39, n. 12, p. 2895–2916, 2012.
- Yuan, C. et al. Dynamical seasonal prediction of Southern African summer precipitation. Climate Dynamics, v. 42, n. 11–12, p. 3357–3374, 2014.
No competing interests reported.
