Ministério da Ciência e Tecnologia Instituto Nacional de Pesquisas Espaciais Centro de Previsão de Tempo e Estudos Climá

1. Ministério da Ciência e Tecnologia Instituto Nacional de Pesquisas Espaciais Centro de Previsão de Tempo e Estudos Climáticos Modelling El Niño-Tropical Cyclones/Hurricanes and Extreme weather

2. Should we expect more extreme weather events? One of the major concerns with a climate change is that an increase in extreme events might occur. Results of observational studies suggest that changes in total precipitation are amplified at the tails, and changes in some temperature extremes have been observed. Model experiments for future climate change show changes in extreme events, such as increases in extreme high temperatures, decreases in extreme low temperatures, and increases in intense precipitation events. On the other hand, for other variables, such as extra-tropical storminess or tropical storms not definite trend could be observed so far. Issues to be considered in the modelling of climate change: Predictability, Skill of the models, resolution.

3. Predictability Key factors affecting interannual variability / predictability in the region, applicable to longer time scale climate precistions. For the oceans- • How can we better predict the phase and amplitude of SST in key areas • What are the respective roles of the dynamics (wind forcing) and thermodynamics (latent heat flux) in the genesis of tropical sea temperature variability • How does ENSO intensity and ‘type’ affect predictability • How is the global ENSO signal transmitted to Africa and at what lead-time • What is the role of subsurface conditions and thermocline adjustments in coupling processes For the atmosphere and land- • What is the atmospheric response to SST variation in key areas, what are the preferred response frequencies How is our knowledge affected by model parameterization • What local land features / indices modulate climate • What are the limits of and spatial distribution of predictability over the continent to assess what components are externally or internally forced • How can forecasters merge multiple predictions in an optimal way • What are the decision variables and how can we best accommodate user needs for climate predictions

4. Standard deviation for rainfall among ensemble members using CPTEC GCM, 10 years, 9 members (Larger values—Lower predictability) DJF MAM JJA SON

5. Rainfall correlation anomaly using CPTEC GCM, 10 years, 9 members Green Values-higher predictability

6. Uncertainties: Uncertainty in projected climate change arises from three main sources: Forcing scenarios: The use of a range of forcing scenarios reflects uncertainties in future emissions and in the resulting greenhouse gas concentrations and aerosol loadings in the atmosphere. Model response: The ensemble standard deviation and the range are used as available indications of uncertainty in model results for a given forcing, although they are by no means a complete characterisation of the uncertainty Missing or misrepresented physics: No attempt has been made to quantify the uncertainty in model projections of climate change due to missing or misrepresented physics. Current models attempt to include the dominant physical processes that govern the behaviour and the response of the climate system to specified forcing scenarios. Model resolution and subgrid-scale processes.

7. Prediction of extremes: Tropical Cyclones and hurricanes The problem of predicting how tropical cyclone frequency might respond to climate change can be broken into two parts: predicting how the prevalence of necessary conditions will change, and predicting how the frequency and strength of potential triggers will change. Given increased concentrations of greenhouse gases, theoretical considerations suggest that the strength of large-scale tropical circulations such as monsoons and trade winds will increase. In general, this would be accompanied by an increase in vertical wind shear, which would hinder the formation of tropical cyclones. On the other hand, more vigorous large-scale circulation might favor more and stronger triggers, such as easterly waves. This would favor more tropical cyclones. Thus the problem is complex, and simple reasoning produces ambiguous results.

8. Problems with simulation of tropical cyclones and their variability GCMs have been used by a number of groups to explore changes in tropical cyclone activity in a world with doubled carbon dioxide (CO2). To date, each group has examined changes in the activity of tropical cyclones produced explicitly by the models. This approach has some drawbacks, because neither the spatial resolution nor the physics of current models is sufficient to accurately simulate tropical cyclones. While the physics of mature model storms may resemble real tropical cyclones, it is unlikely that GCMs realistically mimic tropical cyclone formation, which recent field experiments show to occur on scales as small as 100 miles. The spatial resolution of GCMs is around 200 miles. Nevertheless, GCMs do accurately simulate the frequency of tropical cyclones in the present climate. For climate change scenarios, however, they produce conflicting results. Some of these discrepancies may result from inadequate sampling of tropical cyclones in the model climates. As in the real world, there is much interannual variability in GCM tropical cyclone statistics, making it difficult to extract a representative sample.

9. Should we believe in estimates of climate change and impacts on tropical cyclone activity? Perhaps a better strategy would be to use GCMs to assess the prevalence of necessary conditions and of potential triggers. This would circumvent the need to actually simulate genesis and would be within the bounds of the models' capabilities. (One would have to exercise some care in doing this, since some of these conditions can be expected to vary with climate change. For example, the SST threshold of 26° C would change with global mean temperature). At present, however, there is little basis for accepting quantitative estimates of climate change produced by GCMs, if for no other reason than that there is no basis for believing that they handle water vapor correctly. But there is also good reason to be optimistic about solving the problems that plague current models, and future GCMs should prove to be valuable tools for assessing the effects of climate change on tropical cyclone activity.

10. Will changes in SST and large scale circulation in climate change scenarios would affect tropical cyclone activity? In the current climate, tropical cyclones develop over tropical ocean waters whose SST exceeds about 26°C. But once developed, they may move considerably poleward of these zones. An oft-stated misconception about tropical cyclones is that were the area of 26°C waters to increase, so too would the area experiencing tropical cyclone formation. Thus there is little basis for believing that global warming would substantially expand or contract the area of the world prone to tropical cyclone formation. This is borne out by GCM simulations that show that doubling CO2 substantially increases the area of 26°C waters, but causes no perceptible increase in the area experiencing tropical cyclones. It is conceivable, though, that changes in the large-scale circulation of the atmosphere might increase or decrease the rate at which tropical cyclones move out of their genesis regions and into higher latitudes. It is also likely that changes in atmospheric circulation and sea surface temperature distribution within the tropics would be associated with variations in the distribution of storms.

11. What is the relationship between greenhouse warming, and El Niño/La Niña? • There is a lot of confusion about the interrelations connecting climate phenomena such as El Niño, La Niña and greenhouse effect. Is it true that a warmer atmosphere is likely to produce stronger or more frequent El Niños? • It is certainly a plausible hypothesis that global warming may affect El Niño, since both phenomena involve large changes in the earth's heat balance. However, GCMs are hampered by inadequate representation of many key physical processes (such as the effects of clouds on climate and the role of the ocean). • Also, no computer model yet can reliably simulate BOTH El Niño AND greenhouse gas warming together. So, depending on which model you choose to believe, you can get different answers. For example, some scientists have speculated that a warmer atmosphere is likely to produce stronger or more frequent El Niños, based on trends observed over the past 25 years. However, some computer models indicate El Niños may actually be weaker in a warmer climate.

12. Changes in Variability The capability of models to simulate the large-scale variability of climate, such as the El Niño-Southern Oscillation (ENSO) has improved substantially in recent years, with an increase in the number and quality of coupled ocean-atmosphere models and with the running of multi-century experiments and multi-member ensembles of integrations for a given climate forcing. There have been a number of studies that have considered changes in interannual variability under climate change (e.g., Knutson and Manabe, 1994; Knutson et al., 1997; Tett et al. 1997; Timmermann et al. 1999; Boer et al. 2000b; Collins, 2000a,b). Other studies have looked at intra-seasonal variability in coupled models and the simulation of changes in mid-latitude storm tracks (e.g., Carnell et al. 1996; Lunkeit et al., 1996; Carnell and Senior, 1998; Ulbrich and Christoph, 1999), tropical cyclones (Bengtsson et al., 1996; Henderson-Sellers et al., 1998; Knutson et al., 1998; Krishnamurti et al., 1998; Royer et al., 1998) or blocking anticyclones (Lupo et al., 1997; Zhang and Wang, 1997; Carnell and Senior, 1998). The results from these models must still be treated with caution as they cannot capture the full complexity of these structures, due in part to the coarse resolution in both the atmosphere and oceans of the majority of the models used.

13. Intra-seasonal variability: Daily precipitation variability Changes in daily variability of temperature and rainfall are most obviously manifest in changes in extreme events and much of the work in this area will be discussed in the extreme events section . However, changes in short time-scale variability do not necessarily only imply changes in extreme weather. More subtle changes in daily variability, when integrated over time, could still have important socio-economic impacts. The global mean precipitation also increased, by around 10% in both models, typical of the changes in many mixed-layer models on doubling CO2. An analysis of changes in daily precipitation variability in a coupled model (Durman et al., 2001) suggests a similar reduction in wet days over More recently, there have been several studies looking at changes in intra-seasonal circulation patterns using higher resolution atmosphere-only models with projected SSTs taken from coupled models at given time periods in the future. Theere are some changes in extra-tropical storms on extreme wind and precipitation events and in lower-frequency variability such as persistent or “blocking” anti-cyclones. Changes in African easterly waves may be due to a doubling of CO2 in one model.

14. Standard deviations of Niño-3 SST anomalies (Unit: °C) as a function of time during transient greenhouse warming simulations (black line) from 1860 to 2100 and for the same period of the control run (green line). Minimum and maximum standard deviations derived from the control run are denoted by the dashed green lines. A low-pass filter in the form of a sliding window of 10 years width was used to compute the standard deviations. (a) ECHAM4/OPYC model. Also shown is the time evolution of the standard deviation of the observed from 1860 to 1990 (red line). Both the simulated and observed SST anomalies exhibit trends towards stronger interannual variability, with pronounced inter-decadal variability superimposed, (reproduced from Timmermann et al., 1999), (b) HadCM3 (Collins, 2000b).

15. IInterannual variability and ENSO Climate models have assessed changes that might occur in ENSO in connection with future climate warming and in particular, those aspects of ENSO that may affect future climate extremes. Firstly, will the long-term mean Pacific SSTs shift toward a more El Niño-like or La Niña-like regime? Since 1995, the analyses of several global climate models indicate that as global temperatures increase due to increased greenhouse gases, the Pacific climate will tend to resemble a more El Niño-like state (1999; Boer et al., 2000b). However, the reasons for such a response are varied, and could depend on the modelrepresentation of cloud feedbackor the stronger evaporative damping of the warming in the warm pool region. Secondly, will El Niño variability (the amplitude and/or the frequency of temperature swings in the equatorial Pacific) increase or decrease?. Hu et al. (2001) find that the largest changes in the amplitude of ENSO occur on decadal time-scales with increased multi-decadal modulation of the ENSO amplitude. Several authors have also found changes in other statistics of variability related to ENSO. Collins (2000a) finds an increased frequency of ENSO events and a shift in the seasonal cycle

16. Finally, how will ENSO’s impact on weather in the Pacific Basin and other parts of the world change? Some studies indicate that future seasonal precipitation extremes associated with a given ENSO event are likely to be more intense due to the warmer, more El Niño-like, mean base state in a future climate. That is, for the tropical Pacific and Indian Ocean regions, anomalously wet areas could become wetter and anomalously dry areas become drier during future ENSO events. Also, in association with changes in the extra-tropical base state in a future warmer climate, the teleconnections to mid-latitudes, particularly over North America, may shift somewhat with an associated shift of precipitation and drought conditions in future ENSO events (Meehl et al., 1993). It must be recognised that an “El Niño-like” pattern can apparently occur at a variety of time-scales ranging from interannual to inter-decadal (Zhang et al., 1997), either without any change in forcing or as a response to external forcings such as increased CO2 (Meehl et al., 2000b). Making conclusions about “changes” in future ENSO events will be complicated by these factors. Additionally, since substantial internally generated variability of ENSO statistics on multi-decadal to century time-scales occurs in long unforced climate model simulations (Knutson et al., 1997), the attribution of past and future changes in ENSO amplitude and frequency to external forcing may be quite difficult.

17. The change in 20-year return values for daily maximum (upper panel) and minimum (lower panel) surface air temperature (or screen temperature) simulated in a global coupled atmosphere-ocean model (CGCM1) in 2080 to 2100 relative to the reference period 1975 to 1995 (from Kharin and Zwiers, 2000). Contour interval is 4°C. Zero line is omitted.

18. Changes of Extreme Events Models have improved over time, but they still have limitations that affect the simulation of extreme events in terms of spatial resolution, simulation errors, and parametrizations that must represent processes that cannot yet be included explicitly in the models, particularly dealing with clouds and precipitation. Yet we have confidence in many of the qualitative aspects of the model simulations since they are able to reproduce reasonably well many of the features of the observed climate system not only in terms of means but also of variability associated with extremes Simulations of 20th century climate have shown that including known climate forcings (e.g., greenhouse gases, aerosols, solar) leads to improved simulations of the climate conditions we have already observed. Increased intensity of precipitation events in a future climate with increased greenhouse gases was one of the earliest model results regarding precipitation extremes, and remains a consistent result in a number of regions with improved, more detailed models (Hennessy et al., 1997; Kothavala, 1997; Durman et al., 2001; Yonetani and Gordon, 2001).

19. Simulating a climatology of tropical cyclones Because of their relatively small extent (in global modelling terms) and intense nature, detailed simulation of tropical cyclones for this purpose is difficult. Atmospheric GCMs can simulate tropical cyclone-like disturbances which increase in realism at higher resolution though the intense central core is not resolved (e.g., Bengtsson et al., 1995; McDonald, 1999). Further increases of resolution, by the use of RCMs, provide greater realism (e.g., Walsh and Watterson, 1997) with a very high resolution regional hurricane prediction model giving a reasonable simulation of the magnitude and location of maximum surface wind intensities for the north-west Pacific basin (Knutson et al., 1998). GCMs generally provide realistic simulation of the location and frequency of tropical cyclones. Much effort has gone into obtaining and analysing good statistics on tropical cyclones in the recent past. The main conclusion is that there is large decadal variability in the frequency and no significant trend during the last century. One study looking at the century time-scale has shown an increase in the frequency of North Atlantic cyclones from 1851 to 1890 and 1951 to 1990 (Fernandez-Partagas and Diaz, 1996).

20. Tropical cyclones in a warmer climate Most assessments of changes in tropical cyclone behaviour in a future climate have been derived from GCM or RCM studies of the climate response to anthropogenically-derived atmospheric forcings (Walsh and Katzfey, 2000). Recently, more focused approaches have been used: nesting a hurricane prediction model in a GCM climate change simulation (Knutson et al., 1998); inserting idealised tropical cyclones into an RCM climate change simulation (Walsh and Ryan, 2000). Frequencies increased in the north-west Pacific, decreased in the North Atlantic, and changed little in the south-west Pacific. The likely mean response of tropical Pacific sea surface warming having an El Niño-like structure suggests that the pattern of tropical cyclone frequency may become more like that observed in El Niño years. An indication of the likely changes in maximum intensity of cyclones will be better provided by models able to simulate realistic tropical cyclone intensities. A sample of GCM-generated tropical cyclone cases nested in a hurricane prediction model gave increases in maximum intensity (of wind speed) of 5 to 11% in strong cyclones over the north-west Pacific for a 2.2°C SST warming (Knutson and Tuleya, 1999). T).

21. The very high resolution modelling work suggests that increases in the intensity of tropical cyclones will be accompanied by increases in mean and maximum precipitation rates. In the cases studied, precipitation in the vicinity of the storm centre increased by 20% whereas peak rates increased by 30%. Part of these increases may be due to the increased moisture-holding capacity of a warmer atmosphere but nevertheless point to substantially increasing destructive capacity of tropical cyclones in a warmer climate. Areas of deep convection that can be associated with tropical cyclone formation would not expand with increases in CO2 due to an increase of the SST threshold for occurrence of deep convection (Dutton et al., 2000). Additionally, since tropical storm activity in most basins is modulated by El Niño/La Niña conditions in the tropical Pacific, projections of future regional changes in tropical storm frequencies may depend on accurate projections of future El Niño conditions, an area of considerable uncertainty for climate models. In conclusion, there is some evidence that regional frequencies of tropical cyclones may change but none that their locations will change. There is also evidence that the peak intensity may increase by 5% to 10% and precipitation rates may increase by 20% to 30%. .

22. Changes in extremes of weather and climate Although changes in weather and climate extremes are important to society, ecosystems, and wildlife, it is only recently that evidence for changes we have observed to date has been able to be compared to similar changes that we see in model simulations for future climate (generally taken to be the end of the 21st century). Though several simulations of 20th century climate with various estimates of observed forcings now exist , few of these have been analysed for changes in extremes over the 20th century. So far, virtually all studies of simulated changes in extremes have been performed for future climate. The assessment of extremes here relies on very large-scale changes that are physically plausible or representative of changes over many areas. There are some regions where the changes of certain extremes may not agree with the larger-scale changes. changes in extremes of weather and climate changes in extremes of weather and climate

23. Estimates of confidence in observed and projected changes in extreme weather and climate events. Confidence in observed changes (latter half of the 20th century) Changes in Phenomenon Confidence in projected changes (during the 21st century) Likely Higher maximum temperatures and more hot days a over nearly all land areas Very likely Very likely Higher minimum temperatures, fewer cold days and frost days over nearly all land areas Very likely Likely, over many areas Increase of heat index b over land areas Very likely, over most areas Likely, over many Northern Hemisphere mid- to high latitude land areas More intense precipitation events c Very likely, over many areas Likely, in a few areas Increased summer continental drying and associated risk of drought Likely, over most mid-latitude continental interiors. (Lack of consistent projections in other areas) Not observed in the few analyses available Increase in tropical cyclone peak wind intensities d Likely, over some areas Insufficient data for assessment Increase in tropical cyclone mean and peak precipitation intensities d Likely, over some areas a Hot days refers to a day whose maximum temperature reaches or exceeds some temperature that is considered a critical threshold for impacts on human and natural systems. Actual thresholds vary regionally, but typical values include 32°C, 35°C or 40°C.b Heat index refers to a combination of temperature and humidity that measures effects on human comfort.c For other areas, there are either insufficient data or conflicting analyses.d Past and future changes in tropical cyclone location and frequency are uncertain.

24. Climate variability and extreme events-Global and Regional Climate modelling Global models: Enhanced resolution improves many aspects of the AGCMs’ intra-seasonal variability of circulation at low and intermediate frequencies. However, in some cases values underestimated at standard resolution are overestimated at enhanced resolution. Little sensitivity to resolution in either the interannual or intra-seasonal variability of circulation and precipitation of the South Asian monsoon in HadAM3a. Due to the limited number and length of simulations and a lack of comprehensive analyses, this subject has been almost completely ignored. The only response in variability or extremes that has received any attention is that of tropical cyclones. Regional models: Changes in climate variability between control and 2xCO2 simulations with a nested RCM for the Great Plains of the USA have been reported.Studies have analysed changes in the frequency of heavy precipitation events in enhanced GHG climate conditions over the European region, and suggest an increase of up to several tens of percentage points in the frequency of occurrence of precipitation events exceeding 30 mm/day.

25. Scenario information: Regionalization • Each of the stages of analysis required scenario information to be provided, including: • scenarios of carbon dioxide (CO2) concentration, affecting crop growth and water use, as an input to the crop models; • climate observations and scenarios of future climate, for the crop model simulations; • adaptation scenarios (e.g., new crop varieties, adjusted farm management) as inputs to the crop models; • scenarios of regional population and global trading policy as an input to the trade model.

26. Regionalisation techniques • Three major techniques (referred to as regionalisation techniques) have been developed to produce higher resolution climate scenarios: • regional climate modelling (Giorgi and Mearns, 1991; McGregor, 1997; Giorgi and Mearns, 1999); • statistical downscaling (Wilby and Wigley, 1997; Murphy, 1999); and • high resolution and variable resolution Atmospheric General Circulation Model (AGCM) time-slice techniques (Cubasch et al., 1995; Fox-Rabinovitz et al., 1997). The two former methods are dependent on the large-scale circulation variables from GCMs, and their value as a viable means of increasing the spatial resolution of climate change information thus partially depends on the quality of the GCM simulations. • The variable resolution and high resolution time-slice methods use theAGCMs directly, run at high or variable resolutions.

27. Rainfall estimated by satellite in Venezuela 15-17 December 1999

28. Forecast of rainfall (accumulated 24 hours) for 15 December –Global and regional models MGC CPTEC/COLA T126 (100 km) MGC CPTEC/COLA T062 (200 km) Modelo regional Eta/ (24 horas) Modelo regional Eta/ (60 horas)

29. Incorporation of changes in variability: daily to interannual time-scales • Changes in variability have not been regularly incorporated in climate scenarios because: • less faith has been placed in climate model simulations of changes in variability than of changes in mean climate; • techniques for changing variability are more complex than those for incorporating mean changes; and • there may have been a perception that changes in means are more important for impacts than changes in variability (Mearns, 1995). Techniques for incorporating changes in variability emerged in the early 1990s

30. Other types of variance changes, on an interannual time-scale, based on changes in major atmospheric circulation oscillations, such as ENSO and North Atlantic Oscillation (NAO), are difficult to incorporate into impact assessments. The importance of the variability of climate associated with ENSO phases for resources systems such as agriculture and water resources have been well demonstrated (e.g., Cane et al., 1994; Chiew et al., 1998; Hansen et al., 1998). Where ENSO signals are strong, weather generators can be successfully conditioned on ENSO phases; and therein lies the potential for creating scenarios with changes in the frequency of ENSO events. By conditioning on the phases, either discretely (Wang and Connor, 1996) or continuously (Woolhiser et al., 1993), a model can be formed for incorporating changes in the frequency and persistence of such events, which would then induce changes in the daily (and interannual) variability of the local climate sites. However, it must be noted that there remains much uncertainty in how events such as ENSO might change with climate change.

31. Changes in the frequency of more complex extremes are based on changes in the occurrence of complex atmospheric phenomena (e.g., hurricanes, tornadoes, ice storms). Given the sensitivity of many exposure units to the frequency of extreme climatic events, it would be desirable to incorporate into climate scenarios the frequency and intensity of some composite atmospheric phenomena associated with impacts-relevant extremes. • More complex extremes are difficult to incorporate into scenarios for the following reasons: • high uncertainty on how they may change (e.g., tropical cyclones); • the extremes may not be represented directly in climate models (e.g., ice storms); and • straightforward techniques of how to incorporate changes at a particular location have not been developed (e.g., tropical cyclone intensity at Cairns, Australia).

32. In the case of extremes that are not represented at all in climate models, secondary variables may sometimes be used to derive them. For example, freezing rain, which results in ice storms, is not represented in climate models, but frequencies of daily minimum temperatures on wet days might serve as useful surrogate variables (Konrad, 1998). An example of an attempt to incorporate such complex changes into climate scenarios is the study of McInnes et al. (2000), who developed an empirical/dynamical model that gives return period versus height for tropical cyclone-related storm surges for Cairns on the north Australian coast. To determine changes in the characteristics of cyclone intensity, they prepared a climatology of tropical cyclones based on data drawn from a much larger area than Cairns locally. They incorporated the effect of climate change by modifying the parameters of the Gumbel distribution of cyclone intensity based on increases in tropical cyclone intensity derived from climate model results over a broad region characteristic of the location in question. Estimates of sea level rise also contributed to the modelled changes in surge height.

33. Northeast Brazil: Seasonal Rainfall Comparison Wet Year: 1974 Station Area Averaged Value = 781.7 mm Hulme 0.5 deg Area Averaged Value = 781.6 mm • ECHM overload the rainfall amounts • RSM general pattern is provided by global model • ITCZ further south • ITCZ with strong convective activity • RSM captured the gradient • RSM dry too much RSM Area Averaged Value = 467.4 mm ECHAM Area Averaged Value = 1229.2 mm

34. Seasonal Rainfall Comparison Dry Year: 1983 Hulme 0.5 deg Area Averaged Value = 428.9 mm Station Area Averaged Value = 343.4 mm • ECHM solve only global patterns • Again RSM general pattern is provided by global model • ITCZ further north • ITCZ weak • RSM dry excessively ECHAM Area Averaged Value = 641.1 mm RSM Area Averaged Value = 206.3 mm

35. Seasonal Precipitation Comparison Dry Year (1983)

36. Seasonal Rainfall Comparison: Wet minus Dry (1974-1983) Hulme 0.5 deg Area Averaged Value = 352.7 mm Station Area Averaged Value = 433.5 mm • RSM: Displacement of ITCZ to the south • Better simulation of the difference between the Northeast Region and the Southeast Region than ECHAM ECHAM Area Averaged Value = 601.7 mm RSM Area Averaged Value = 261.0 mm

37. Zooming in the interest region Wet Year (1974) • RSM was able to reproduce qualitatively the rainfall's gradient between the northwest and southeast • RSM failed positioning the maximum of rain

38. Zooming in the interest region Dry Year (1983) • RSM captured the "idea" of wet in Maranhao against dry southeastward

39. Number of events that produced more than 10 mm/day Wet Year(1974) FEB MAR APR RSM SUDENE

40. Number of events that produced more than 10 mm/day Dry Year(1983) FEB MAR APR RSM SUDENE • Both cases RSM shows a dry month along the season

41. Region 2 Region 1 Daily Evolution Dry Season (1983) Region 2 SUDENE ECHAM RSM Rainfall 24h (mm) Region 1 Rainfall 24h (mm) MAR FEB APR