The Intertropical Convergence Zone Over the Middle East and North Africa: Detection and Trends

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The Intertropical Convergence Zone over the Middle East and North Africa: Detection and Trends

Thesis by Anna Ailene Scott

In Partial Fulllment of the Requirements

For the Degree of

Masters of Science

King Abdullah University of Science and Technology, Thuwal,

Kingdom of Saudi Arabia The thesis of Anna Ailene Scott is approved by the examination committee

Committee Chairperson: Georgiy Stenchikov

Committee Member: Matthew McCabe

Committee Member: Stoitchko Kalenderski

2 Copyright c 2013 Anna Ailene Scott

3 ABSTRACT

The Intertropical Convergence Zone over the Middle East and North

Africa: Detection and Trends

Anna Ailene Scott

This thesis provides an overview of identifying the Intertropical Convergence Zone (ITCZ) in the

Middle East and North Africa (MENA) region. The ITCZ is a zone of wind convergence around the equator that coincides with an area of intense precipitation that is commonly termed a tropical rainbelt. In Africa, these two concepts are frequently confounded. This work studies the correlation between precipitation and commonly used ITCZ indicators. A further attempt is made to detect movement in the African ITCZ, based on earlier paleontological studies showing historical changes in precipitation. Zonally averaged wind convergence is found to be the most reliable indicator of the African ITCZ, one having a low correlation with zonally averaged precipitation. Precipitation is found only to be a reliable indicator for the African ITCZ in zones near the wind convergence, which reaches as far north as 20◦N in the summer. No secular change in location of the African ITCZ is found for the time of available data. Finally, historical data shows that any increase in precipitation in the Sahel, a region where precipitation is driven by the ITCZ, is mildly negatively correlated with precipitation in the rainbelt area, suggesting that shifts in the ITCZ result in a widening of the precipitation prole as well as a shift of the entire zone.

4 ACKNOWLEDGEMENTS

I would like to extend a special thanks to Prof. Stenchikov for his help and support with this research, as well as to my committee members for generously donating their time. I would also like to thank my friends, classmates and colleagues, whose help and technical support was invaluable these past months at KAUST.

5 Contents

Examination Committee Approval 2

Abstract 4

Acknowledgements 5

1 Introduction 10

2 Background on data sources 14 2.1 Reanalysis Data ...... 15 2.1.1 ERA-Interim ...... 15 2.2 Observation Data ...... 16 2.2.1 TRMM ...... 16 2.2.2 LIS-OTD ...... 18 2.3 Historical Data ...... 18 2.3.1 Nicholson's Dataset ...... 18

3 ITCZ identication methods 20 3.1 Introduction ...... 20 3.2 Identication methods ...... 21 3.2.1 Wind ...... 21 3.2.1.1 Surface wind patterns ...... 21 3.2.1.2 Zonally averaged meridional wind ...... 21 3.2.2 Rainfall ...... 24 3.2.3 Lightning ...... 27 3.2.4 Convergence ...... 29 3.3 Relationship between convergence, wind and precipitation ...... 31 3.4 Conclusions ...... 42

4 ITCZ shift 43 4.1 From historical data ...... 43 4.2 ERA-Interim ...... 46 4.3 Conclusions ...... 49

6 5 Conclusions 51

Bibliography 52

7 List of Figures

1.1 Greening of the Sahel from (author?) [13]. The results of trend analyses of time series of NDVI amplitude (top) and NDVI seasonal integral (bottom) of NOAA AVHRR NDVI-data from 1982 to 1999. Areas with trends of <95% probability in white. Data from 40 climate observation stations, showing percent change between the periods 19821990 and 19911999, have been superimposed on the top gure...... 13

2.1 ERA-Interim RMS forecast errors for (a) tropical wind vectors and (b) temperatures at 200 hPa, averaged over all forecasts issued daily at noon UTC in 1989, for ERA- Interim (red), ERA-40 (blue), and for ECMWF forecasting system (green). Forecast errors are relative to a xed set of radiosonde observations. (c,d) are as (a,b) but for 850 hPa. From (author?) [3]...... 15 2.2 Number of observations assimilated into ERA-Interim's atmospheric analysis component, daily, on a logarithmic scale. From (author?) [3] ...... 17

3.1 Low-level wind vectors in Africa for August (left), January (right). 900hPa (top), 950hPa (middle), 1000hPa (bottom) ...... 22 3.2 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the global tropics (left), tropics land (middle), and tropical oceans (right), for January (bold blue), April (green), July (bold red), Octo- ber (light blue), and annual average (black). Methodology follows that of (author?) [22]...... 23 3.3 Number of wet months in MENA since 1997 from TRMM monthly data. Wet months are dened as months having average precipitation rates of 0.01mm/hr or higher. . 24 3.4 Histogram of rainfall rates over MENA during TRMM period, 1997-2011 ...... 25 3.5 Composite of rainfall rate time series from each grid point in MENA during TRMM period, 1997-2011 ...... 26 3.6 Linear trends in TRMM precipitation rates over TRMM period, 1997-2011 . . . . . 26 3.7 Lightning ash rate density full climatology, for 1997-2011, from LIS-OTD dataset. 27 3.8 Correlation between TRMM anomaly rainfall rate and LIS-OTD anomaly lightning ash rate ...... 28 3.9 Correlation between TRMM rainfall rate and LIS-OTD lightning ash rate . . . . . 29 3.10 Dierence in time-averaged lighting ash rate between rst half of TRMM period (1997-2004) and second half (2004-2011) ...... 30

8 3.11 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Africa for January (bold blue), April (green), July (bold red), October (light blue), and annual average (black)...... 32 3.12 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for West Africa. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black). 33 3.13 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for East Africa. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black). 34 3.14 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Ethiopian Highlands. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black)...... 36 3.15 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Arabian Peninsula. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black)...... 37 3.16 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Atlas Mountains...... 38 3.17 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for Central Africa...... 39 3.18 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Western Sahel, north...... 40 3.19 Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Western Sahel, south...... 41

4.1 Interregional correlations for nineteenth century Africa. Point to point correlation of precipitation anomaly (time series of yearly data, 1800-1899) between starred region and other regions of Africa. Starred regions all belong to Sahel...... 44 4.2 Interregional correlations for wet years in Africa. Point to point correlation of precipitation anomaly (time series of yearly data, years when the starred region experiences ooding) between starred region and other regions of Africa. Starred regions all belong to Sahel...... 45 4.3 Time series of the ITCZ latitude on a submonthly scale, using zonally averaged convergence maximum and the zero crossing point of meridional wind data as indicators for ITCZ location. For tropical East Africa...... 47 4.4 Time series of the ITCZ latitude on a submonthly scale, using zonally averaged convergence maximum and the zero crossing point of meridional wind data as indicators for ITCZ location in tropical West Africa...... 47 4.5 Composite time series of African precipitation at each longitude point along 20◦N (ERA-Interim Data). Dierent colors represent dierent locations...... 48 4.6 Composite time series of African precipitation at each longitude point along 20◦N (ERA-Interim Data). Dierent colors represent dierent locations...... 48

9 Chapter 1

Introduction

The Intertropical Convergence Zone, or ITCZ, marks the zone of tropical Hadley cell-related upwelling where northern and southern trade winds converge near the equator and rise, producing intense convection, clouds and rainfall. The Hadley cell circulation is a simple overturning convection mechanism, driven by solar radiation. Solar radiation in the tropics warms air, which rises and spreads polewards before subsiding and then owing equator-wards again. As incoming solar radiation (insolation) is most intense in the tropics, the Hadley cells are centered around the equator, where the location of convergence and upwelling marks the ITCZ.

While the Hadley cell circulation picture is an idealization, it is one that works well when speaking of a global zonal average. These features are even visible to the naked eye the ITCZ can be recognized qualitatively in satellite images as a thick band of clouds and storms that encircles the

Earth. The phenomena is most apparent over the Pacic where the ITCZ is clearly dened. This denition can be lost over the land masses, particularly Africa.

The ITCZ is certainly a key component of global atmospheric circulation, but its connection to tropical precipitation has more direct impact on human interests. The ITCZ includes the tropical rainbelt, a region of dense clouds, abundant storms, and frequent rain. This rainfall is of critical importance to the health and wellbeing of people around the world; indeed, it is estimated that

tropical rainfall patterns control the subsistence lifestyle of more than one billion people, [19]. In

Africa, this is even more so the case, as in many regions, such as the Sahel, the shift of the ITCZ results in a wet period that brings 75 − 90% of the regions rainfall [8]. For human communities, rarely as mobile as convective cells are dynamic, the lack of access to this rainfallwhether due to geopolitics, geography or lack of irrigation technologycan be particularly disastrous, and there is

10 a long history of drought-related famines wreaking havoc. Thus any change in rainfall intensity, rainfall location, or date of rainfall arrival can have outsize consequences on an already sensitive region.

Quantitatively, the ITCZ can be recognized as a local maxima in convergence, a local minimum in outgoing long wave radiation (OLR), the zero point for meridional wind, or global maxima in precipitation, so long as these variables are examined averaged over the globe. Local variations and disruptions caused by land formations makes the precise demarcation of the ITCZ dicult. Unlike other atmospheric phenomenon, which can be identied by precise indicators, no such index exists for the ITCZ. Dierent authors use dierent denitions, and more problematically, these various diagnostics can conate causes and eects.

Additionally, a recent study of West Africa using NCEP reanalysis data [9] is challenging the classical notion that the ITCZ system produces most of the rainfall in the ITCZ, suggesting instead that the tropical rainbelt in the region is produced between the African Easterly Jet and the Tropical

Easterly Jet. A key nding in 9 is that low-level and surface convergence is eectively independent of the zone of maximum precipitation. The analysis, performed using NCEP Reanalysis Data, focused primarily on West Africa, though they did note that convergence-linked rainfall did aect the southern Sahara and northern Sahel.

This work attempts to examine methods of identication African ITCZ, with a specic focus on the Middle East and North Africa (MENA) region. Using each of the popular denitions (low- level wind convergence and precipitation) and some novel ones (meridional wind and lightning) an attempt will be made to pinpoint the ITCZ zone, with a particular aim to examine its seasonal and interannual evolution. An additional aim is to test the relationship between large scale circulation features and local precipitation to evaluate the dynamical claims of 9. A particular emphasis will be placed on the Arabian peninsula as well as East Africa. The study will be carried out using a mixture of observation data, reanalysis data, and historical data. The results will verify whether the global dynamics explanation of the ITCZ holds over land in MENA, the Sahel, and the Sahara.

In addition to seasonal shifts, the ITCZ moves on century and millenial time scales. Currently, the ITCZ is located slightly north of the equator. As the ITCZ is driven by solar radiative heating, its seasonal location varies in response with the location of the maximum insolation, and thus the

ITCZ moves north in the summertime and south in the wintertime. The current global average for the location is approximately 10◦N in the boreal summertime and 3◦N in the boreal wintertime [19]. This has not been the case historically: during the Holocene, the ITCZ was located further south in response to Northern Hemisphere cooling, and has moved northwards since the end of the Little Ice

11 Age, suggesting the potential for climatic warming to again induce a northward shift of the ITCZ [4].

Indeed, recent numerical experiments from Professor Stenchikov's group have suggested exactly this, showing a future northward movement of various ITCZ indicators meridional wind zero crossing, precipitation, and OLR in response to warming. The movement was identied comparing average data from 2025-2050 to similarly averaged data from the period 2000-2025.

Such results may occur in the future due to increased CO2 -related climate change. But identifying any secular trend will be complicated by the inherently noisy nature of ITCZ variability in

Africa. Natural ITCZ variations occur on decadal and centennial time scales in response to local and global forcings and feedback mechanisms. Interdecadal variability in the Sahel precipitation record is well known, and reconstructions from sediment cores in Ghana's Lake Bosumtwi suggest that severe droughts in the Sahel may last for decades to centuries, the most common sub-century scale variation lasting around 40 years [20]. While the seasonal shift is abrupt and thus easy to detect, any permanent interannual shift would be dicult to detect on decadal timescales as the timescales of any natural variations dwarf the timescale of the available observation data.

In recent years, greening has been detected in the Sahel region [13], the arid region south of the Sahara. The dry conditions of the 1980s and early 1990s brought a period of drought, famine and war in many Sahelian nations that was catastrophic for the region, but perhaps not out of step with the historical variability of the region. Wetter conditions in the late 1990s and earlier part of this century have since prevailed in some regions of the Sahel, but again, such variability is not statistically signicant in either the short or long term. The increase in vegetation cover is more measurable and apparent across the Sahel. This increase in vegetation greenness is measured by the Normalized Dierence Vegetation Index (NDVI), which estimates the prevelance live green vegetation through an area's reectivities in the visible and infrared spectrums. Such a change is noteworthy because it was detected through simple linear analysis and in the absence of signicant trends in rainfall. It is thus unclear whether this greening trend is due to changes in agricultural practices, population migration, natural variability or climate change. Nevertheless, the trend is zonally contiguous across Africa at around 12◦N and the strongest evidence to date for a modern shift of the ITCZ.

12 Figure 1.1: Greening of the Sahel from (author?) [13]. The results of trend analyses of time series of NDVI amplitude (top) and NDVI seasonal integral (bottom) of NOAA AVHRR NDVI- data from 1982 to 1999. Areas with trends of <95% probability in white. Data from 40 climate observation stations, showing percent change between the periods 19821990 and 19911999, have been superimposed on the top gure.

13 Chapter 2

Background on data sources

This study uses a mix of observation, reanalysis and model output data. The observational record is scarce over North Africa; available data is sparsely sampled in space and time. Even data from population centers and ports is unavailable. Modern satellite products like the Tropical Rainfall

Measuring Mission (TRMM) dataset have only two decades of coverage. For less historically conventional variables like convergence and wind, even the very recent modern record is unavailable.

For the purposes of measuring the location of the ITCZ, regular spaced information is imperative, or is at least more than a few data points.

This necessitates the use of either model output or reanalysis data. Reanalysis uses a mix of forecast models, data assimilation methods and observation inputs to generate a dataset that coheres with both atmospheric dynamics and physical observations. Essentially, reanalysis uses weather observations to correct the model output and interpolate this information at locations where there are no observation stations. For examining historical trends, it is preferable to model data because of its increased accuracy, and for areas like MENA where observation stations are sparsely sampled, it is the closest available product to observation data.

For variables like temperature and precipitation, the luck is better, albeit minimally so. Not only is there a longer available observation record, but there is more variety in terms of reanalysis products. New datasets like the Nicholson dataset [11] have increased the historical observation record for precipitation, and satellite products like TRMM ll in the gaps of the tropical spatial record. However, even these observations pose problems. Nicholson's dataset presents precipitation using a wetness index, corresponding to standard deviations away from the mean, complicating any matching process between theirs and more modern datasets. TRMM poses problems as well because

14 Figure 2.1: ERA-Interim RMS forecast errors for (a) tropical wind vectors and (b) temperatures at 200 hPa, averaged over all forecasts issued daily at noon UTC in 1989, for ERA-Interim (red), ERA-40 (blue), and for ECMWF forecasting system (green). Forecast errors are relative to a xed set of radiosonde observations. (c,d) are as (a,b) but for 850 hPa. From (author?) [3]. the satellite only passes overhead any given point a limited number of times a day, and thus may miss certain weather events. While the orbital path varies the time of day at which the satellite passes overhead, in some regions, notably the Arabia Peninsula, rainfall is so intermittent that satellites may completely miss an area's only rainfall event. Thus, reanalysis data is still invaluable as it oers a relatively long temporal record of consistently sampled and regularly spaced data.

2.1 Reanalysis Data

2.1.1 ERA-Interim

ERA-Interim is a global atmospheric reanalysis dataset produced by the European Center for

Medium-Range Weather Forecasts (ECMWF) and extending back to January 1, 1989, intended as an improvement on the earlier ERA-40 dataset. Of particular interest for this study are the im- provements made in the hydrological cycle. ERA-Interim uses a 4D -Var assimilation together with the EMCWF weather forecast model; the model runs on 12-hour cycles, using a variational analysis of atmospheric parameters as initial conditions for the next cycle. Included in this analysis are

15 observations, from around 106 observations daily in 1989 and increasing with time (Fig. 2.2). Most of the volume of data comes from satellites and is for clear-sky radiance measurements, atmospheric motion data, wind data and ozone data. Conventional in situ observations like temperature, wind and specic humidity come from airplanes, radiosondes and weather balloons. Similar 2-meter data and 10-meter data for wind come from ships, buoys and land observation stations. All observations are passed through a numerical error nding algorithm to ensure they are error free; information from unreliable weather stations or failing satellite sensors or statistically unlikely information is excluded. Finally, the 4D-Var minimization function limits the inuence on outliers to further limit any implausible data [3].

While there is little data to compare for some of the variables used in this work, the ERA-Interim global errors show that it is at the very least an improvement over the ERA-40 data and the EMCWF forecasts (Fig. 2.1).

The dataset used for this study is gridded monthly means of daily means on a global dataset with a grid spacing of 1.5◦ for both latitude and longitude, and 37 pressure levels. ECMWF ERA-Interim data used in this study have been obtained from the ECMWF Data Server.

2.2 Observation Data

2.2.1 TRMM

The Tropical Rainfall Measuring Mission (TRMM) is satellite mission monitoring tropical rainfall and run by NASA and the Japanese space agency NASDA. The satellite also carries instruments measuring lightning, which will be addressed in the next section. The satellite was launched in

November 1997 and monitors areas between 35◦S and 35◦N, initially from an orbit at 350km and then moved in 2001 to 402.5km. The satellite uses a precipitation radar (PR), a microwave imager (TMI) and a visible and infrared scanner (VIRS) to produce an estimate of rainfall rates at the surface as well as vertical precipitation proles. The TRMM orbit is non sun-synchronous, that is, will pass over any given point at dierent times during the day, and thus covers the northern and southern most latitudes more than the equatorial points, but the satellite makes approximately 16 passes over the earth each day and has 90 seconds to take measurements of a point before it passes out of the eld of view. The minimum sensitivity of the PR instrument is 15 dBZ which translates into a minimum surface precipitation accuracy of 0.1 mm/hr [4, 5, 6, 2].

16 Figure 2.2: Number of observations assimilated into ERA-Interim's atmospheric analysis component, daily, on a logarithmic scale. From (author?) [3]

17 2.2.2 LIS-OTD

The LIS-OTD product is a lightning ash frequency density dataset, using data measured from the

LIS (Lightning Imaging Sensor) instrument mounted on TRMM and the older and less sensitive

Optical Transient Detector, which covered a larger range of latitudes. LIS uses a wide-eld optical lens and lters out background light, taking an image and processing the data in real-time. The system can detect about 90% of all ashes occurring within the eld of view, though weak daytime signals are often not caught. The rate, expressed as a ash rate density (fl/km2/hr) is determined as the number of ashes seen at a given location during the time the satellite has that location in its eld of view, which is approximately 80 seconds.

The particular product in question is the LIS/OTD 2.5 degree low resolution time series, as well as the annual climatology for the same resolution.. The dataset describes lightning ash rates gridded to a 2.5 by 2.5 degree (latitude/longitude) on a daily time series over the span of the various missions, OTD from February 1995- March 2000 and LIS spanning from January 1998 to December

2011.

The v1.0 gridded satellite lightning data were produced by the NASA LIS/OTD Science Team

(Principal Investigator, Dr. Hugh J. Christian, NASA / Marshall Space Flight Center) and are available from the Global Hydrology Resource Center (http://ghrc.msfc.nasa.gov).

2.3 Historical Data

2.3.1 Nicholson's Dataset

This rain gauge dataset, described in (author?) [12], measures rainfall variability for 90 regions of Africa, translating physical gauge data and two centuries worth of qualitative observations into a seven-level wetness index that loosely measures standard deviations from the mean. Only the nineteenth century part of the dataset is addressed in this work for availability reasons. Spatial coverage is boosted with principal component analysis. Regions are selected which are "homogeneous with respect to interannual variability" so that any information from the region is assumed to describe the entire region. The dataset compiles a wealth of disparate descriptions of local climate, using colonial gauge data, missionary diaries, and explorers' records, applying the notion that while individual records may be subject to undesirable variability, once multiple accounts converge to a coherent description, then the records are accurate. Descriptive information is then translated to the wetness index, with values ranging from −3 to 3. For example, a classication of −3 corresponds to a

18 drought extreme enough to cause human consequences like migration or famine, and a classication of 0 corresponding to a normal year, and a classication of 3 corresponding to extreme ooding. Quantitative information is transferred onto the same scale using standard deviations. Missing regional values are inferred using the spatial correlation between surrounding areas, with a threshold correlation of .4 over 100 years of data. Lastly, a principal component method is used to reconstruct any information not available from the previous methods. The range of the dataset is annual data from 1801-1900.

19 Chapter 3

ITCZ identication methods

3.1 Introduction

The ITCZ can be identied using any number of criteria, all of which are grounded in some physical reasoning. Frequently used criteria include local OLR minima, vegetation indices, convergence maxima, precipitation maxima, and the zero crossing of the meridional wind. All of these are taken to be indicators of convergence and the precipitation that is supposed to accompany it; outgoing long wave radiation minima locate the thick cloud band that encircles the globe and coincides with a zone of heavy rainfall, while the zero crossing of the meridional wind indicates where the meridional wind changes direction as a proxy for convergence.

The following indicators will be examined for their suitability in identifying the ITCZ: lightning, low-level convergence, low-level meridional wind, and precipitation. This work focuses on the relationship between circulation and precipitation, thus the focus will be on examining coherence between convergence and precipitation. The maximum precipitation indicator is one of the most popular, but perhaps also the most problematic for Africa. It reduces a phenomena of measurable width to a single location, and assumes that convergence and precipitation are directly linked. Thus

ITCZ indicators will be evaluated not only for their suitability to determine the mean ITCZ location, but also the northern and southern bounds.

The criteria for determining suitability are as follows: rst, the indicator must be well dened in the mathematical sense. That is, given that observations show that the ITCZ is unique, the indicator must give either a unique latitude or a continuous range of latitudes. This range of latitudes must be quantitatively or qualitatively distinct from other zones. Second, the indicator must follow the

20 seasonal shift of the ITCZ, showing movement north in the summer and south in the winter. Third, the indicator must show good agreement with other ITCZ phenomena. Even if certain methods of identifying the ITCZ are more convenient than others, the ITCZ can be recognized by any number of phenomenon. Thus a reasonable amount of harmony and correlation is to be expected between the various parameters.

3.2 Identication methods

3.2.1 Wind

3.2.1.1 Surface wind patterns

First, we examine the wind data to see if it is reasonable to zonally average and average over the pressure levels. Splitting the ERA-Interim wind vector data into January and August data, the data are then averaged over time to produce an average summer and average winter picture (see Fig.3.1).

The wind patterns are more coherent for January than they are for August, revealing a clear band around 10◦N that extends to the horn of Africa. In August, this band is shifted north of 15◦N and meanders to the other side of the continent.

3.2.1.2 Zonally averaged meridional wind

The ITCZ designates where the meridional (North/South) winds converge. Thus, a reasonable indicator of this process occurring is the changing sign of the meridional component of the wind vector, and approach used by (author?) [22]. Surface wind patterns are quite noisy, and the convergence zone is a three dimensional phenomena, so multiple levels are looked at. Averaging over altitude combines information of what is occuring at multiple levels. Zonally averaging serves some of the same purpose. While the surface wind patterns in Fig. 3.1 may seem to lack zonal coherence, averaging should give more sense to some of the local incoherence in wind patterns.

To study this, the v wind component from ERA-Interim dataset was averaged over pressure levels 900 to 1000hPa and zonally averaged over the global tropics. Then, the data was separated by month (January, April, July, October) and time averaged over entire the ERA-Interim period to have an average picture of divergence. The average over all months was also calculated. The results from this as well as from performing similar analysis on precipitation and divergence can be seen in Fig. 3.2. This methodology over the global tropics reveals the expected global prole of precipitation. The annual precipitation average reaches its peak at 6◦N, winter precipitation is

21 Figure 3.1: Low-level wind vectors in Africa for August (left), January (right). hPa (top), hPa 22 900 950 (middle), 1000hPa (bottom) Figure 3.2: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the global tropics (left), tropics land (middle), and tropical oceans (right), for January (bold blue), April (green), July (bold red), October (light blue), and annual average (black). Methodology follows that of (author?) [22] .

23 Figure 3.3: Number of wet months in MENA since 1997 from TRMM monthly data. Wet months are dened as months having average precipitation rates of 0.01mm/hr or higher. maximal at about 4◦N and summer precipitation is maximal at around 9◦, reecting the seasonal shift in precipitation. The plots also show the increase of precipitation in summer, reecting the increase in precipitation in Africa and India during the July/August monsoon and rainy seasons.

The zonally averaged meridional wind proles match this seasonal march, marching north in the summer and retreating south in the winter. In particular, the zero crossing of the wind matches exactly with the precipitation maximum as well as the convergence maximum for January. For July data, this zero crossing is located farther north than either of the other indicators, more so over land than over the ocean. Thus, while the zero crossing of the meridional wind may not give the same results as precipitation or convergence, it can reasonably reproduce the seasonal shift of the ITCZ.

Furthermore, its northward shift over land may make it useful as a potential bound for the ITCZ.

3.2.2 Rainfall

It is a standard convention to refer to the ITCZ using only the location of maximum rainfall. Rainfall over Africa is maximal in the ITCZ region, and moves seasonally in conjunction with a change in

24 Figure 3.4: Histogram of rainfall rates over MENA during TRMM period, 1997-2011 insolation and the corresponding convergence patterns. Maximum rainfall is located around the equator when viewed over all seasons, and when examined seasonally, maximum rainfall is located slightly south of the equator in the winter and moves to about 10◦N in the summer. A sharp gradient in precipitation can be seen around 15◦N, showing the northern limit of where precipitation falls. North of this is the Sahara, the largest desert in Africa, which receives little to no precipitation.

As the MENA region is dominated by the Sahara, the most common precipitation pattern is no precipitation, and thus changes are hard to detect. The distribution of rainfall in MENA is dominated by zero observations (Fig. 3.4), and highly non-normal. While it may seem that any non-zero observation oers the possibility to detect change, such events are randomly distributed.

For example, calculating linear trends for rainfall (Fig. 3.6) shows no clear patterns, and in many regions, the trends using TRMM rainfall rate are badly scaled. The time series of the data is not amenable to linear trend analysis as any linear t method must solve a nearly singular matrix system. The anomaly data has random peaks that cannot be t into a linear curve (Fig. 3.5); perhaps cyclical patterns exist, however, as natural variations exist on timescales longer than this dataset, nothing can be said for certain. It should be noted that rainfall rates in the MENA region can be so low that they are not within the sensitivity of the TRMM sensor, however, for the most part, these rain rates are mostly zero (Fig. 3.4).

25 Figure 3.5: Composite of rainfall rate time series from each grid point in MENA during TRMM period, 1997-2011

Figure 3.6: Linear trends in TRMM precipitation rates over TRMM period, 1997-2011

26 Figure 3.7: Lightning ash rate density full climatology, for 1997-2011, from LIS-OTD dataset.

3.2.3 Lightning

The lightning and precipitation data come from instruments on the same satellite having approximately the same eld of view. Thus, it is reasonable to expect some degree of correlation between

ash rate and precipitation rate if it is reasonable to expect convective precipitation (precipitation linked to storm systems) to be linked to precipitation. In other regions, this is exactly the case.

Over the North Pacic, for example, the the relationship is logarithmic, that is, rainfall rates increase linearly with the logarithm of the lightning ash rate [15].

The statistical correlation is grounded in physical reality; lightning develops in clouds high enough for ice particles to form. Evaporation of precipitation on the descending branch of the Hadley cell prevents clouds from forming over the Sahara, while evaporation rates for precipitation in wet areas produce conditions suitable for cloud formation. Lightning strikes do not occur in areas without clouds, thus lightning itself is a sort of indicator of wetness and cloudiness, both possible ITCZ identiers. This can be seen in the LIS-OTD observation data for lightning over Africa, where a marked discontinuity is visible in the lighting ash rate at approximately 15◦N (Fig. 3.7). Just as precipitation undergoes a seasonal migration northward, so too does the lightning ash rate. While lightning oers a reasonable assessment of cloud position, its most interesting feature lies in its potential connection to precipitation. Most lightning ashes do not result in lightning strikes and unlike precipitation, do not have a direct impact on human health and well being. Thus, lightning is interesting not only as an indicator of cloud formation, but as an indicator of wetness and precipitation. To study this connection, monthly lightning ash rate data was correlated to monthly rainfall at each point in space over the MENA domain. Results indicate that lightning

27 Figure 3.8: Correlation between TRMM anomaly rainfall rate and LIS-OTD anomaly lightning ash rate

ash rate density and precipitation rate in the same area show very little correlation (see Fig. 3.9).

From this, we can conclude that lightning is not a very good indicator of rainfall over the MENA region. This is not a very surprising result given the scarcity of rainfall in the region, however physically reasonable the two phenomena may be. The relationship between precipitation anomaly and lightning ash rate anomaly was also made, showing very little correlation over land (see Fig.3.8).

Higher correlation values are more prevalent over the ocean.

To look at the change in time, the last seven years of lightning data were subtracted from the

rst seven (Fig. 3.10), some spatial patterns emerge over Africa. A decrease in lightning ash rates is seen over West Africa, while over East Africa, the trend is an increase. This pattern lasts from the equator up through the Sahel, and stops at the Sahara. No trend is visible in the Sahara, most likely due to the scarcity of lightning ashes in that region.

28 Figure 3.9: Correlation between TRMM rainfall rate and LIS-OTD lightning ash rate

3.2.4 Convergence

To measure convergence, the ERA-Interim dataset was averaged over pressure levels 900 to 1000hPa and zonally averaged over the global tropics. Instead of convergence, divergence was examined, taking the minimum divergence to represent maximum convergence. As performed for wind, the data was separated by month (January, April, July, October) and time averaged to have an average picture of divergence. The average over all months was also calculated.

The divergence data (Fig. 3.2) shows a similar prole to that of precipitation for the annual data.

Divergence attains a minimum at approximately 6◦N. While this minimum is approximately situated between the January and July curves, unlike that of precipitation data, the annual average minimum has approximately the same magnitude as that of the July minima. The January minima, located at approximately 2◦N, is shifted slightly north compared to the January precipitation maxima, and in contrast to the precipitation data, convergence is highest in January, and lower in July. As the annual average peak nearly matches that of the July prole, convergence does not shift more than a couple degrees north to 4◦. The spring and fall proles match best with the July proles.

29 Figure 3.10: Dierence in time-averaged lighting ash rate between rst half of TRMM period (1997-2004) and second half (2004-2011)

30 3.3 Relationship between convergence, wind and precipitation

Similar calculations were performed for the continent of Africa, as well as smaller regions. To more closely study the pehnomena, calculations were performed on a very local scale. First, Africa was split into two zones, east and west, along 20◦E. To localize the phenomena, these calculations were also performed for various other regions, including the African CORDEX (Co-Ordinated Regional

Downscaling EXperiment) regions- the Arabian Peninsula, the Ethiopian highlands, the Atlas mountains, Central Africa, Western Sahel North, Western Sahel South. These regions were less conclusive in their results.

When the area range is restricted to Africa, dened as 40◦N to 40◦S and 15◦W to 35◦E, the same calculations reveal similar patterns as in the global case, but more dierences between the seasons (Fig. 3.3). The precipitation maximum shifts from around 5◦S in January to 10◦N in July. However, the divergence proles are less coherent, exhibiting both smaller scale and larger scale oscillations. January divergence data has two minima, at approximately 4◦N and 6◦N, much father north than the 5◦S January precipitation maximum. The divergence shifts to a maximum at

19◦N in July. This maximum is also larger in magnitude than that in January, in contrast to the global prole where January divergence was maximal. These maximum locations also correspond approximately with the zero meridional wind crossing location. Thus, for Africa, convergence and zonal wind crossing seasonal patterns are dierent than precipitation patterns.

When we perform separate calculations for western and eastern Africa, dierences emerge between the regions. The western Africa prole (Fig. 3.12) matches the African average in the Northern hemisphere nearly exactly for precipitation as well as divergence. For eastern Africa, the precipitation proles lacks the thick band that exhibited by the precipitation prole in continental average and western African. Rather, precipitation is a sharp peak, the maximum located a few degrees south of the equator in January and around 10◦N in July. Divergence in eastern Africa also changes (Fig. 3.3). The January minimum is located around

8◦N, shifting to 20◦N in July. The divergence magnitude is maximal in April, though the precipitation prole of April very closely matches that of January. This is in contrast to western Africa, where the divergence magnitude is maximal in October. Additionally, the convergence proles in western Africa dier in magnitude between January and July; in eastern Africa, the January and

July magnitudes are very similar.

In Ethiopia (Fig. 3.3), the month with maximum precipitation is April, with April and July having the next highest rainfall. However, in July, there is barely any convergence in this region;

31 Figure 3.11: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Africa for January (bold blue), April (green), July (bold red), October (light blue), and annual average (black).

32 Figure 3.12: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for West Africa. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black).

33 Figure 3.13: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for East Africa. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black).

34 divergence crosses the x-axis at 12◦N but only just while in January there is, the maximum being at

12◦N. Thus convergence is maximal in January, although the precipitation is less by a factor of two. Precipitation increases in July, but its maximum location does not shift seasonally and remains at

8◦N annually. This approximately corresponds to the zero meridional wind crossing. In the Arabian peninsula, there is no precipitation in January (Fig. 3.3). Precipitation increases slightly in April, attains its maximum in July and then falls o slightly in October. There is very little convergence over the Arabia peninsula in January, and in July, the maximum convergence rests at 17◦N. The convergence lasts until about 22◦N, and north of 20◦N there is no precipitation. In this region, the maximum July convergence matches the zero meridional wind crossing; i.e., the meridional wind changes direction at 17◦N as well. In the Atlas mountains (Fig. 3.3), July is a dry season with little rain. All other months have similar precipitation proles.

In Central Africa (Fig. 3.3), the rainy season shift is apparent, with the July precipitation maximum being located around 8◦N and the January precipitation maximum is located south of the limits of the region. However, there is no convergence in July, and the convergence is maximal in January, its maximum at 6◦N. In the Western Sahel (North) (Fig. 3.3), there is no precipitation in January, and precipitation is maximal in July, the maximum location at 9◦N. Only in April does the zero meridional crossing enter the region. Convergence is only taking place during January, with a maximum at 9◦N, and in

April, at 12◦N. In the Western Sahel (South) (Fig. 3.3), there is a small amount of precipitation in January, more in July, and most in October. The zero meridional wind crossing only appears in January, at

8◦N. There is only convergence in this region, with no maxima and minima. January convergence is higher than all other months north of 6◦N. The summary of the relationship between precipitation, convergence and zero meridional wind crossing in July is summarized in Table 3.1.

No regions share the same clear picture as the tropical average. While precipitation shifts north in every region excepting the Atlas, convergence is only present in July over the Arabian Peninsula and the African average. The zero-wind crossing point is only located inside the Arabian Peninsula and Ethiopian highlands in July, as well as the African average. Thus, while Africa as a whole experiences a July precipitation shift accompanied by a shift of convergence and the meridional wind crossing, the only CORDEX region with this phenomena is the Arabian peninsula.

35 Figure 3.14: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Ethiopian Highlands. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black).

36 Figure 3.15: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Arabian Peninsula. Colors show the months: January (bold blue), April (green), July (bold red), October (light blue), and annual average (black).

37 Figure 3.16: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Atlas Mountains.

38 Figure 3.17: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for Central Africa.

39 Figure 3.18: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Western Sahel, north.

40 Figure 3.19: Zonally averaged proles of precipitation (rst row), meridional wind (second row), and divergence (third row) for the Western Sahel, south.

41 Table 3.1: Relationship between July Convergence, Precipitation and Meridional wind zero-crossing

max P in July? P north in July? C. in July? max C in July? V0 in July? Seasonal change? Tropics (land) Yes Yes Yes No No Yes Africa No Yes Yes Yes Yes Yes Arabian P. Yes Yes Yes Yes Yes Yes Atlas No No No No No Yes C. Africa Yes Yes No No No Yes Ethiopian H. Yes Yes No No Yes No WA-HN Yes Yes(?) No No No Yes WA-SS Yes Yes Yes No No Yes

3.4 Conclusions

Convergence showed the clearest indicators of the ITCZ. It often disagreed precipitation data, though the seasonal shift was in agreement. Wind data showed high correlation values with precipitation but qualitatively was not clear. Lightning data showed more agreement with other circulation indicators, rather than precipitation. Thus, to examine movement of the ITCZ, convergence data will be used.

42 Chapter 4

ITCZ shift

An ITCZ shift should be detected when there is warming. However, what form this shift will take on is unclear. Hopefully, such a shift can be manifested in terms of convergence, meridional wind, and rainfall. Rainfall is the only variable that exists going back in time, but it is unclear what kind of change it will undergo. There could be decadal or century scale drought, that is, a drying of the entire continent. Alternatively, a shifting ITCZ could result in a change in the location and distribution of precipitation, this eect being much easier to measure.

4.1 From historical data

First, the plausibility of the shift is tested using historical precipitation data. If we observe an ITCZ shift northwards, then we should see an increase of rain in the Sahara/Sahel region and a decrease of rain in the southern regions. That is, the rain should be zero-sum, in order to control for years of continent-wide drought, for example, as in the early parts of the 1800's. First, we examine the correlation between the Sahara/Sahel regions and all the other regions of Africa for all years. We group the Sahel/Sahara region as regions north of 15◦N as there is a sharp negative gradient in precipitation rate in this region (Fig. 3.3). The results, in Figure 4.1, show a strong correlation between precipitation anomaly in neighboring regions of the Sahara and the Sahel, with moderate anti-correlations to southern regions, but very little statistically signicant correlations.

Next, we compare this to the correlation for wet years, that is, years in which the Sahara/Sahel regions have moderate to severe wetness and ooding, dened as a year having a wetness index of

−2 or −3. For these years (Fig. 4.2), the minimum correlation increases. This is especially the case in the Sahel regions of East Africa. This means that wet years in the Sahara and Sahel regions

43 Figure 4.1: Interregional correlations for nineteenth century Africa. Point to point correlation of precipitation anomaly (time series of yearly data, 1800-1899) between starred region and other regions of Africa. Starred regions all belong to Sahel.

44 Figure 4.2: Interregional correlations for wet years in Africa. Point to point correlation of precipitation anomaly (time series of yearly data, years when the starred region experiences ooding) between starred region and other regions of Africa. Starred regions all belong to Sahel.

45 are more anti-correlated with wet years in the typical ITCZ belt area than are average years. This indicates that precipitation anomalies in MENA over the century scale can be seen as zero-sum; that is, when the Sahel becomes wetter, the usual tropical rain belt area becomes drier. This indicates that a shift of the rain belt is occurring during years when the Sahel is becoming wetter.

4.2 ERA-Interim

Next, the ITCZ position is calculated using the observation record, providing a locator of the monthly

ITCZ position. Using the zero meridional wind crossing and maximum wind convergence as location indicators, the position of the ITCZ was mapped as a function of time over the duration of the ERA-

Interim period, from 1989 to 2012. The results lead to two conclusions: rst, that the ITCZ path is located far north of where the bulk of precipitation falls, and second, that the ITCZ summer and winter maximum and minimum positions are largely unchanged for the entirety of the observation data record. This indicates that over this time period, there is no change in the ITCZ path.

Additionally, conducting separate analysis for dierent regions of Africa shows that dynamical patterns do not hold constant across the continent. In tropical East Africa, the zero meridional wind crossing matches nearly exactly with wind divergence. This is not the case in tropical West Africa, where the zero meridional wind crossing appears mostly south of the equator- though occasionally migrating north of the equator- but convergence remains in the northern hemisphere. Thus wind convergence is a much more reliable indicator of ITCZ location than is meridional wind. The time series shows approximately the same lack of annual variability for convergence, though the zero meridional wind crossing location is much more variable over time.

These results show that convergence occurs far north of the precipitation maximum. While convergence has dynamical implications, precipitation has perhaps more serious agricultural, political and humanitarian consequences, and so measuring its change in response to climate change is of greater interest. As convergence is bounded by the twentieth latitude in the north, this is a natural parallel to study sensitivity to change. A composite precipitation time series from all data points along 20◦N is shown in Figures4.5 and 4.6; these results show how variable precipitation is in the Sahel region as well as the importance of looking at a longer timeframe. The results from ERA-

Interim data (Fig. 4.5) show wetter conditions in the 1990's then in the early part of this century;

TRMM conclusions are harder to draw as it is hard to determine a baseline precipitation level from the short record. Both records show abnormally dry and out of phase rain conditions in 2000.

46 Figure 4.3: Time series of the ITCZ latitude on a submonthly scale, using zonally averaged convergence maximum and the zero crossing point of meridional wind data as indicators for ITCZ location. For tropical East Africa.

Figure 4.4: Time series of the ITCZ latitude on a submonthly scale, using zonally averaged convergence maximum and the zero crossing point of meridional wind data as indicators for ITCZ location in tropical West Africa.

47 Figure 4.5: Composite time series of African precipitation at each longitude point along 20◦N (ERA- Interim Data). Dierent colors represent dierent locations.

Figure 4.6: Composite time series of African precipitation at each longitude point along 20◦N (ERA- Interim Data). Dierent colors represent dierent locations.

48 4.3 Conclusions

The idealized, classical picture of the ITCZ suggests that tropical precipitation is driven by surface convergence. As the trade winds converge, they rise and form clouds, resulting in precipitation.

Subsequent work in the mid twentieth century argued that while convergence occurs along a large zone, the moist layer is the deciding factor in generating a layer thick enough to support strong convection and thus maximum cloudiness and minimum OLR occur closer to the equator than precipitation[9]. If only continental averages are considered, then this classical picture explains the production of rainfall, as convergence and meridional wind proles match reasonably well with the precipitation proles. However, examining Africa as separate regions shows that surface and low level convergence cannot cause the bulk of the rainfall produced over Africa. While convergence and precipitation proles may match for the tropical ocean, and over tropical land, precipitation maximum at least corresponds to a local convergence maximum, in Africa, neither of these are true.

With the exception of the Arabian Peninsula region, local convergence maxima do not agree with local precipitation maxima. In East Africa, the summertime ITCZ is located north of 20◦N, even though there are few places with detectable precipitation events north of 15◦N (Fig. 3.3). Over West Africa, the zero crossing of meridional wind occurs much farther south than the maximum divergence, neither of which is particularly linked to precipitation.

A more important result is that while precipitation is scarce in the Arabian Peninsula, the

July convergence, meridional wind and precipitation proles cohere with one another. The zone of convergence, from about 13◦N to about 22◦N, gives a northern bound on precipitation, which is minimal north of 20◦, if not the southern bound- precipitation increases in the south of the region, and keeps increasing south towards Yemen. The zero crossing point of the meridional wind is located approximately at 17◦N, corresponding nearly exactly with the convergence maximum. In January, there is no precipitation, meridional wind does not change direction, and there is no convergence.

Thus, low level convergence is an important factor in determining precipitation in the Arabian

Peninsula.

None of the other CORDEX regions lie at the latitude of average surface convergence, however, as convergence is consistently located around 20◦N in the summertime, is is reasonable to extrapolate this nding to the other regions at this latitude. That is, convergence is located much farther north than the classic ITCZ rainbelt across the continent, and thus the rainfall directly related to convergence must be located primarily in these locations. This would appear to explain the dry conditions north of the region, as in the classical Hadley cell circulation picture, air away from the

49 zone of convergence appears to lose most of its moisture. However, the circulation picture is clearly not meridionally symmetric, as the zone south of convergence receives most of the rainfall.

This is not to say that the ITCZ is not linked intimately with tropical rainfall in Africa. The coupled seasonal jump present in all variables shows some degree of correlation. However, more important correlations lie with other dynamical features. These lessons only serve to underscore the dierence between the marine ITCZ and the ITCZ over land.

In terms of examining long term change, this revised picture of the ITCZ may improve detection of warming related trends. Instead of examining the shifts of maximum rainfall near the equator, it suces to look at rainfall around 20◦N. Such regions in Africa receive so little rainfall that any change is easy to detect, however, it is paramount to establish a reasonable rainfall baseline before drawing any conclusions. At least two decades of data is necessary, and even then, this only demonstrates relative change; wet and dry periods can last this long or even longer, for example, the Sahel droughts of the 1970's and 1980's.

50 Chapter 5

Conclusions

In this study, various approaches were considered for characterizing and identifying the ITCZ over

Africa and determining any trends in its movement. Historical changes in mean ITCZ location have typically been measured using proxies for precipitation, taking cores from lakebeds to calculate records going back millenia. In recent years, the ITCZ-aected Sahel region in Africa has undergone greening that is measurable from the satellite-derived NDVI. Precipitation patterns in Africa vary cyclically, with the most frequent variation lasting around forty years, and the 1980s and early 1990s brought severe droughts to the Sahel, so in this sense, greening after a prolonged dry period is not unexpected.

What is signicant is the novel way of detecting such a shift. Thanks to satellite products and advances in numerical models, high-resolution gridded data is available in an area not known for the density of its weather monitoring. Even with the relative abundance of data, however, the short temporal record makes determining secular trends dicult.

The most successful case in the literature of identifying a zonally contiguous secular change in the

ITCZ region is with NDVI data. However, no analysis was made to connect this to any shift of the

ITCZ. Paleo reconstructions of precipitation have also shown century scale dry and wet conditions, pointing to shifts in the ITCZ, though due to the lack of data, no denitive conclusion can be drawn.

Determining a change in more than one feature, of preference convergence as well as precipitation, is neccessary to conclude that a change is related in the changing ITCZ. Thus, modern data oers the most potential for showing change over a range of diagnostic variables.

To this end, surface convergence and meridional wind patterns from reanalysis data were analyzed for various regions in Africa and compared to the seasonal patterns of rainfall. Additionally, historical

51 rainfall data was examined to see how precipitation changes over time. The two results showed seemingly contradictory ndings- while surface convergence is independent of the tropical rainbelt for land surfaces over Africa, precipitation increases in the northern Sahel and Sahara are likely be accompanied by precipitation decreases in the tropical rainbelt. That is, the classic picture of the ITCZ over Africa- one which places the bulk of tropical rain production at the same location as convergence- does not hold at the local level. However, when precipitation increases in the Sahel and

Sahara, precipitation tends to decrease in the tropical rainbelt area, suggesting that the zone where rain falls can shift north and is detectable from examining anomaly rainfall data. Thus, changes in the African ITCZ can be seen on its northern limits, changes which manifest themselves in an increase or decrease of precipitation in the Arabian Peninsula, Sahel, and Sahara.

These regions, some of the driest on earth, are particularly sensitive to changes in precipitation cycles. The eect of climate change on such areas is yet to be seen, given the short length of the observation record. Furthermore, natural variations in drought cycles in Africa may cause wet and dry periods that last decades or centuries. On longer timescales, however, it historically consistent to expect the ITCZ to move north in response to warming. Such movement is undetectable at present, though it seems likely that such a change will result in an increase in precipitation in dry regions in

MENA, and possible that this will be accompanied by drying in the tropical rainbelt.

Understanding local wind dynamics should play a key role in any forecasting or modelling for the region. MENA is not zonally coherent, and classic explanations of tropical dynamics fail to fully account for the bulk of the region's rainfall production. Such classic circulation pictures, however appropriate over the ocean, are unhelpful over land in Africa. More detailed data are needed for the zone of maximum wind convergence, often around 20◦ N in the summertime. Such data can help monitor any changes in the ITCZ, and help predict the impact of climate change on a critically sensitive area.

For further work, numerical experiments are recomended to examine the eect of convergence location on precipitation, with a particular focus on East Africa and the Arabian Peninsula. A more detailed understanding of local atmospheric dynamics may improve forecasting in drought sensitive regions, and validation with observation data may help provide feedback to improve the model. Of particular interest would be a dynamical study focusing on precipitation variability in

East Africa. Such work could help identify the jets and monsoon conditions that control droughts and wet periods. A twentieth century historical model run could be veried using the Nicholson

2012 dataset or other available rain guage datasets such as the Global Precipitation Climatology

Centre (GPCC) precipitation dataset.

52 Bibliography

[1] R. F. Adler, G. J. Human, D. T. Bolvin, S. Curtis, and E. J. Nelkin. Tropical rainfall dis-

tributions determined using TRMM combined with other satellite and rain gauge information.

Journal of Applied Meteorology, 39(12):20072023, 2013/04/13 2000.

[2] S. A. Braun. TRMM senior review proposal. Technical report, NASA Goddard, 2007.

[3] D. P. Dee, S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A.

Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot,

N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy,

H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. McNally,

B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.-N.

Thépaut, and F. Vitart. The European Center for Medium Range Weather Forecast Reanalysis:

conguration and performance of the data assimilation system. Quarterly Journal of the Royal

Meteorological Society, 137(656):553597, 2011.

[4] A. Koutavas and J. Lynch-Stieglitz. Variability of the marine ITCZ over the eastern Pacic

during the past 30,000 years: Regional perspective and global context. In H. F. Diaz and

R. S. Bradley, editors, The Hadley Circulation: Present, Past, and Future, Advances in Global

Change Research, pages 347369. Springer Netherlands, 2004.

[5] C. Kummerow, W. Barnes, T. Kozu, J. Shiue, and J. Simpson. The tropical rainfall measuring

mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology, 15(3):809

817, 2013/04/26 1998.

[6] C. Kummerow, J. Simpson, O. Thiele, W. Barnes, A. T. C. Chang, E. Stocker, R. F. Adler,

A. Hou, R. Kakar, F. Wentz, P. Ashcroft, T. Kozu, Y. Hong, K. Okamoto, T. Iguchi, H. Kuroiwa,

E. Im, Z. Haddad, G. Human, B. Ferrier, W. S. Olson, E. Zipser, E. A. Smith, T. T. Wilheit,

G. North, T. Krishnamurti, and K. Nakamura. The status of the tropical rainfall measuring

53 mission (TRMM) after two years in orbit. Journal of Applied Meteorology, 39(12):19651982,

2013/04/26 2000.

[7] D. W. Lea, D. K. Pak, L. C. Peterson, and K. A. Hughen. Synchroneity of tropical and high-

latitude atlantic temperatures over the last glacial termination. Science, 301(5638):13611364,

2003.

[8] T. Lebel, A. Diedhiou, and H. Laurent. Seasonal cycle and interannual variability of the sahelian

rainfall at hydrological scales. J. Geophys. Res., 108(D8), 04 2003.

[9] S. Nicholson. A revised picture of the structure of the "monsoon" and land ITCZ over Wfrica

africa. Climate Dynamics, 32(7-8):11551171, 2009.

[10] S. E. Nicholson. Climatic and environmental change in africa during the last two centuries.

Climate Research, 17(2):123144, 2001.

[11] S. E. Nicholson, A. K. Dezfuli, and D. Klotter. A two-century precipitation dataset for the con-

tinent of africa. Bulletin of the American Meteorological Society, 93(8):12191231, 2013/04/26

2012.

[12] S. E. Nicholson, A. K. Dezfuli, and D. Klotter. A two-century precipitation dataset for the con-

tinent of africa. Bulletin of the American Meteorological Society, 93(8):12191231, 2013/04/26

2012.

[13] L. Olsson, L. Eklundh, and J. Ardö. A recent greening of the saheltrends, patterns and

potential causes. Journal of Arid Environments, 63(3):556566, 11 2005.

[14] A. H. Oort and J. J. Yienger. Observed interannual variability in the hadley circulation and its

connection to enso. Journal of Climate, 9(11):27512767, 2013/04/26 1996.

[15] A. T. Pessi and S. Businger. Relationships among lightning, precipitation, and hydrometeor

characteristics over the north Pacic ocean*. Journal of Applied Meteorology and Climatology,

48(4):833848, 2013/05/11 2009.

[16] S. G. H. Philander, D. Gu, G. Lambert, T. Li, D. Halpern, N.-C. Lau, and R. C. Pacanowski.

Why the ITCZ Is Mostly North of the Equator. Journal of Climate, 9:29582972, Dec. 1996.

[17] R. Ramel, H. Gallée, and C. Messager. On the northward shift of the West African monsoon.

Climate Dynamics, 26:429440, 2006. 10.1007/s00382-005-0093-5.

54 [18] E. E. Riddle and D. S. Wilks. Statistical indices of the northward rainfall progression over

eastern africa. International Journal of Climatology, 33(2):356375, 2013.

[19] J. P. Sachs, D. Sachse, R. H. Smittenberg, Z. Zhang, D. S. Battisti, and S. Golubic. Southward

movement of the Pacic intertropical convergence zone AD1400-1850. Nature Geosci, 2(7):519

525, 07 2009.

[20] T. M. Shanahan, J. T. Overpeck, K. J. Anchukaitis, J. W. Beck, J. E. Cole, D. L. Dettman,

J. A. Peck, C. A. Scholz, and J. W. King. Atlantic forcing of persistent drought in Wfrica

africa. Science, 324(5925):377380, 2009.

[21] T. Suzuki. Seasonal variation of the ITCZ and its characteristics over Central africa. Theoretical

and Applied Climatology, 103(1-2):3960, 2011.

[22] N. agar, G. Skok, and J. Tribbia. Climatology of the ITCZ derived from ERA-interim reanal-

yses. Journal of Geophysical Research: Atmospheres, 116(D15):n/an/a, 2011.