The Role of the Cloud Radiative Effect in the Sensitivity of the Intertropical Convergence Zone to Convective Mixing


Talib, J., S.J. Woolnough, N.P. Klingaman, and C.E. Holloway, 2018: The Role of the Cloud Radiative Effect in the Sensitivity of the Intertropical Convergence Zone to Convective Mixing. J. Climate, 31, 6821–6838,

Rainfall in the tropics is commonly associated with the Intertropical Convergence Zone (ITCZ), a discontinuous line of convergence collocated at the ascending branch of the Hadley circulation, where strong moist convection leads to high rainfall. What controls the location and intensity of the ITCZ remains a fundamental question in climate science.

Figure 1: Annual-mean, zonal-mean tropical precipitation (mm day-1) from Global Precipitation Climatology Project (GPCP, observations, solid black line) and CMIP5 (current coupled models) output. Dashed line indicates CMIP5 ensemble mean.

In current and previous generations of climate models, the ITCZ is too intense in the Southern Hemisphere, resulting in two annual-mean, zonal-mean tropical precipitation maxima, one in each hemisphere (Figure 1).  Even if we take the same atmospheric models and couple them to a world with only an ocean surface (aquaplanets) with prescribed sea surface temperatues (SSTs), different models simulate different ITCZs (Blackburn et al., 2013).

Within a climate model parameterisations are used to replace processes that are too small-scale or complex to be physically represented in the model. Parameterisation schemes are used to simulate a variety of processes including processes within the boundary layer, radiative fluxes and atmospheric chemistry. However my work, along with a plethora of others, shows that the representation of the ITCZ is sensitive to the convective parameterisation scheme (Figure 2a). The convective parameterisation scheme simulates the life cycle of clouds within a model grid-box.

Our method of showing that the simulated ITCZ is sensitive to the convective parameterisation scheme is by altering the convective mixing rate in prescribed-SST aquaplanet simulations. The convective mixing rate determines the amount of mixing a convective parcel has with the environmental air, therefore the greater the convective mixing rate, the quicker a convective parcel will become similar to the environmental air, given fixed convective parcel properties.

Figure 2: Zonal-mean, time-mean (a) precipitation rates (mm day-1}$) and (b) AEI (W m-2) in simulations where the convective mixing rate is varied.

In our study, the structure of the simulated ITCZ is sensitive to the convective mixing rate. Low convective mixing rates simulate a double ITCZ (two precipitation maxima, orange and red lines in Figure 2a), and high convective mixing rates simulate a single ITCZ (blue and black lines).

We then associate these ITCZ structures to the atmospheric energy input (AEI). The AEI is the amount of energy left in the atmosphere once considering the top of the atmosphere and surface energy budgets. We conclude, similar to Bischoff and Schneider, 2016, that when the AEI is positive (negative) at the equator, a single (double) ITCZ is simulated (Figure 2b). When the AEI is negative at the equator, energy is needed to be transported towards the equator for equilibrium. From a mean circulation perspective, this take place in a double ITCZ scenario (Figure 3). A positive AEI at the equator, is associated with poleward energy transport and a single ITCZ.

Figure 3: Schematic of a single (left) and double ITCZ (right). Blue arrows denote energy transport. In a single ITCZ scenario more energy is transported in the upper branches of the Hadley circulation, resulting in a net-poleward energy transport. In a double ITCZ scenario, more energy is transport equatorward than poleward at low latitudes, leading to an equatorward energy transport.

In our paper, we use this association between the AEI and ITCZ to hypothesize that without the cloud radiative effect (CRE), atmospheric heating due to cloud-radiation interactions, a double ITCZ will be simulated. We also hypothesize that prescribing the CRE will reduce the sensitivity of the ITCZ to convective mixing, as simulated AEI changes are predominately due to CRE changes.

In the rest of the paper we perform simulations with the CRE removed and prescribed to explore further the role of the CRE in the sensitivity of the ITCZ. We conclude that when removing the CRE a double ITCZ becomes more favourable and in both sets of simulations the ITCZ is less sensitive to convective mixing. The remaining sensitivity is associated with latent heat flux alterations.

My future work following this publication explores the role of coupling in the sensitivity of the ITCZ to the convective parameterisation scheme. Prescribing the SSTs implies an arbitary ocean heat transport, however in the real world the ocean heat transport is sensitive to the atmospheric circulation. Does this sensitivity between the ocean heat transport and atmospheric circulation affect the sensitivity of the ITCZ to convective mixing?

Thanks to my funders, SCENARIO NERC DTP, and supervisors for their support for this project.


Blackburn, M. et al., (2013). The Aqua-planet Experiment (APE): Control SST simulation. J. Meteo. Soc. Japan. Ser. II, 91, 17–56.

Bischoff, T. and Schneider, T. (2016). The Equatorial Energy Balance, ITCZ Position, and Double-ITCZ Bifurcations. J. Climate., 29(8), 2997–3013, and Corrigendum, 29(19), 7167–7167.


Baroclinic and Barotropic Annular Modes of Variability


Modes of variability are climatological features that have global effects on regional climate and weather. They are identified through spatial structures and the timeseries associated with them (so-called EOF/PC analysis, which finds the largest variability of a given atmospheric field). Examples of modes of variability include El Niño Southern Oscillation, Madden-Julian Oscillation, North Atlantic Oscillation, Annular modes, etc. The latter are named after the “annulus” (a region bounded by two concentric circles) as they occur in the Earth’s midlatitudes (a band of atmosphere bounded by the polar and tropical regions, Fig. 1), and are the most important modes of midlatitude variability, generally representing 20-30% of the variability in a field.

Figure 1: Southern Hemisphere midlatitudes (red concentric circles) as annulus, region where annular modes have the largest impacts. Source.

We know two types of annular modes: baroclinic (based on eddy kinetic energy, a proxy for eddy activity and an indicator of storm-track intensity) and barotropic (based on zonal mean zonal wind, representing the north-south shifts of the jet stream) (Fig. 2). The latter are usually referred to as Southern (SAM or Antarctic Oscillation) or Northern (NAM or Arctic Oscillation) Annular Mode (depending on the hemisphere), have generally quasi-barotropic (uniform) vertical structure, and impact the temperature variations, sea-ice distribution, and storm paths in both hemispheres with timescales of about 10 days. The former are referred to as BAM (baroclinic annular mode) and exhibit strong vertical structure associated with strong vertical wind shear (baroclinicity), and their impacts are yet to be determined (e.g. Thompson and Barnes 2014, Marshall et al. 2017). These two modes of variability are linked to the key processes of the midlatitude tropospheric dynamics that are involved in the growth (baroclinic processes) and decay (barotropic processes) of midlatitude storms. The growth stage of the midlatitude storms is conventionally associated with increase in eddy kinetic energy (EKE) and the decay stage with decrease in EKE.

Figure 2: Barotropic annular mode (right), based on zonal wind (contours), associated with eddy momentum flux (shading); Baroclinic annular mode (left), based on eddy kinetic energy (contours), associated with eddy heat flux (shading). Source: Thompson and Woodworth (2014).

However, recent observational studies (e.g. Thompson and Woodworth 2014) have suggested decoupling of baroclinic and barotropic components of atmospheric variability in the Southern Hemisphere (i.e. no correlation between the BAM and SAM) and a simpler formulation of the EKE budget that only depends on eddy heat fluxes and BAM (Thompson et al. 2017). Using cross-spectrum analysis, we empirically test the validity of the suggested relationship between EKE and heat flux at different timescales (Boljka et al. 2018). Two different relationships are identified in Fig. 3: 1) a regime where EKE and eddy heat flux relationship holds well (periods longer than 10 days; intermediate timescale); and 2) a regime where this relationship breaks down (periods shorter than 10 days; synoptic timescale). For the relationship to hold (by construction), the imaginary part of the cross-spectrum must follow the angular frequency line and the real part must be constant. This is only true at the intermediate timescales. Hence, the suggested decoupling of baroclinic and barotropic components found in Thompson and Woodworth (2014) only works at intermediate timescales. This is consistent with our theoretical model (Boljka and Shepherd 2018), which predicts decoupling under synoptic temporal and spatial averaging. At synoptic timescales, processes such as barotropic momentum fluxes (closely related to the latitudinal shifts in the jet stream) contribute to the variability in EKE. This is consistent with the dynamics of storms that occur on timescales shorter than 10 days (e.g. Simmons and Hoskins 1978). This is further discussed in Boljka et al. (2018).

Figure 3: Imaginary (black solid line) and Real (grey solid line) parts of cross-spectrum between EKE and eddy heat flux. Black dashed line shows the angular frequency (if the tested relationship holds, the imaginary part of cross-spectrum follows this line), the red line distinguishes between the two frequency regimes discussed in text. Source: Boljka et al. (2018).


Boljka, L., and T. G. Shepherd, 2018: A multiscale asymptotic theory of extratropical wave, mean-flow interaction. J. Atmos. Sci., in press.

Boljka, L., T. G. Shepherd, and M. Blackburn, 2018: On the coupling between barotropic and baroclinic modes of extratropical atmospheric variability. J. Atmos. Sci., in review.

Marshall, G. J., D. W. J. Thompson, and M. R. van den Broeke, 2017: The signature of Southern Hemisphere atmospheric circulation patterns in Antarctic precipitation. Geophys. Res. Lett., 44, 11,580–11,589.

Simmons, A. J., and B. J. Hoskins, 1978: The life cycles of some nonlinear baroclinic waves. J. Atmos. Sci., 35, 414–432.

Thompson, D. W. J., and E. A. Barnes, 2014: Periodic variability in the large-scale Southern Hemisphere atmospheric circulation. Science, 343, 641–645.

Thompson, D. W. J., B. R. Crow, and E. A. Barnes, 2017: Intraseasonal periodicity in the Southern Hemisphere circulation on regional spatial scales. J. Atmos. Sci., 74, 865–877.

Thompson, D. W. J., and J. D. Woodworth, 2014: Barotropic and baroclinic annular variability in the Southern Hemisphere. J. Atmos. Sci., 71, 1480–1493.

Climate model systematic biases in the Maritime Continent


The Maritime Continent commonly refers to the groups of islands of Indonesia, Borneo, New Guinea and the surrounding seas in the literature. My study area covers the Maritime Continent domain from 20°S to 20°N and 80°E to 160°E as shown in Figure 1. This includes Indonesia, Malaysia, Brunei, Singapore, Philippines, Papua New Guinea, Solomon islands, northern Australia and parts of mainland Southeast Asia including Thailand, Laos, Cambodia, Vietnam and Myanmar.

Figure 1: JJA precipitation (mm/day) and 850 hPa wind (m s−1) for (a) GPCP and ERA-interim, (b) MMM biases and (c)–(j) AMIP biases for 1979–2008 over the Maritime Continent region (20°S–20ºN, 80°E–160ºE). Third panel shows the Maritime Continent domain and land-sea mask

The ability of climate model to simulate the mean climate and climate variability over the Maritime Continent remains a modelling challenge (Jourdain et al. 2013). Our study examines the fidelity of Coupled Model Intercomparison Project phase 5 (CMIP5) models at simulating mean climate over the Maritime Continent. We find that there is a considerable spread in the performance of the Atmospheric Model Intercomparison Project (AMIP) models in reproducing the seasonal mean climate and annual cycle over the Maritime Continent region. The multi-model mean (MMM) (Figure 1b) JJA precipitation and 850hPa wind biases with respect to observations (Figure 1a) are small compared to individual model biases (Figure 1c-j) over the Maritime Continent. Figure 1 shows only a subset of Fig. 2 from Toh et al. (2017), for the full figure and paper please click here.

We also investigate the model characteristics that may be potential sources of bias. We find that AMIP model performance is largely unrelated to model horizontal resolution. Instead, a model’s local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon.

Figure 2: Latitude-time plot of precipitation zonally averaged between 80°E and 160°E for (a) GPCP, (b) Cluster I and (c) Cluster II. White dashed line shows the position of the maximum precipitation each month. Precipitation biases with respect to GPCP for (d) Cluster I and (e) Cluster II.

To characterize model systematic biases in the AMIP runs and determine if these biases are related to common factors elsewhere in the tropics, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I (Figure 2b,d) is able to reproduce the observed seasonal migration of Maritime Continent precipitation, but it overestimates the precipitation, especially during the JJA and SON seasons. Cluster II (Figure 2c,e) simulate weaker seasonal migration of Intertropical Convergence Zone (ITCZ) than observed, and the maximum rainfall position stays closer to the equator throughout the year. Tropics-wide properties of clusters also demonstrate a connection between errors at regional scale of the Maritime Continent and errors at large scale circulation and global monsoon.

On the other hand, comparison with coupled models showed that air-sea coupling yielded complex impacts on Maritime Continent precipitation biases. One of the outstanding problems in the coupled CMIP5 models is the sea surface temperature (SST) biases in tropical ocean basins. Our study highlighted central Pacific and western Indian Oceans as the key regions which exhibit the most surface temperature correlation with Maritime Continent mean state precipitation in the coupled CMIP5 models. Future work will investigate the impact of SST perturbations in these two regions on Maritime Continent precipitation using Atmospheric General Circulation Model (AGCM) sensitivity experiments.




Jourdain N.C., Gupta A.S., Taschetto A.S., Ummenhofer C.C., Moise A.F., Ashok K. (2013) The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations. Climate Dynamics. 41(11–12):3073–3102

Toh, Y.Y., Turner, A.G., Johnson, S.J., & Holloway, C.E. (2017). Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble. Climate Dynamics. doi: 10.1007/s00382-017-3641-x

Should we be ‘Leaf’-ing out vegetation when parameterising the aerodynamic properties of urban areas?


When modelling urban areas, vegetation is often ignored in attempt to simplify an already complex problem. However, vegetation is present in all urban environments and it is not going anywhere… For reasons ranging from sustainability to improvements in human well-being, green spaces are increasingly becoming part of urban planning agendas. Incorporating vegetation is therefore a key part of modelling urban climates. Vegetation provides numerous (dis)services in the urban environment, each of which requires individual attention (Salmond et al. 2016). However, one of my research interests is how vegetation influences the aerodynamic properties of urban areas.

Two aerodynamic parameters can be used to represent the aerodynamic properties of a surface: the zero-plane displacement (zd) and aerodynamic roughness length (z0). The zero-plane displacement is the vertical displacement of the wind-speed profile due to the presence of surface roughness elements. The aerodynamic roughness length is a length scale which describes the magnitude of surface roughness. Together they help define the shape and form of the wind-speed profile which is expected above a surface (Fig. 1).


Figure 1: Representation of the wind-speed profile above a group of roughness elements. The black dots represent an idealised logarithmic wind-speed profile which is determined using the zero-plane displacement (zd) and aerodynamic roughness length (z0) (lines) of the surface.

For an urban site, zd and z0 may be determined using three categories of methods: reference-based, morphometric and anemometric. Reference-based methods require a comparison of the site to previously published pictures or look up tables (e.g. Grimmond and Oke 1999); morphometric methods describe zd and z0 as a function of roughness-element geometry; and, anemometric methods use in-situ observations. The aerodynamic parameters of a site may vary considerably depending upon which of these methods are used, but efforts are being made to understand which parameters are most appropriate to use for accurate wind-speed estimations (Kent et al. 2017a).

Within the morphometric category (i.e. using roughness-element geometry) sophisticated methods have been developed for buildings or vegetation only. However, until recently no method existed to describe the effects of both buildings and vegetation in combination. A recent development overcomes this, whereby the heights of all roughness elements are considered alongside a porosity correction for vegetation (Kent et al. 2017b). Specifically, the porosity correction is applied to the space occupied and drag exerted by vegetation.

The development is assessed across several areas typical of a European city, ranging from a densely-built city centre to an urban park. The results demonstrate that where buildings are the dominant roughness elements (i.e. taller and occupying more space), vegetation does not obviously influence the calculated geometry of the surface, nor the aerodynamic parameters and the estimated wind speed. However, as vegetation begins to occupy a greater amount of space and becomes as tall as (or larger) than buildings, the influence of vegetation is obvious. Expectedly, the implications are greatest in an urban park, where overlooking vegetation means that wind speeds may be slowed by up to a factor of three.

Up to now, experiments such as those in the wind tunnel focus upon buildings or trees in isolation. Certainly, future experiments which consider both buildings and vegetation will be valuable to continue to understand the interaction within and between these roughness elements, in addition to assessing the parameterisation.


Grimmond CSB, Oke TR (1999) Aerodynamic properties of urban areas derived from analysis of surface form. J Appl Meteorol and Clim 38:1262-1292.

Kent CW, Grimmond CSB, Barlow J, Gatey D, Kotthaus S, Lindberg F, Halios CH (2017a) Evaluation of Urban Local-Scale Aerodynamic Parameters: Implications for the Vertical Profile of Wind Speed and for Source Areas. Boundary-Layer Meteorology 164: 183-213.

Kent CW, Grimmond CSB, Gatey D (2017b) Aerodynamic roughness parameters in cities: Inclusion of vegetation. Journal of Wind Engineering and Industrial Aerodynamics 169: 168-176.

Salmond JA, Tadaki M, Vardoulakis S, Arbuthnott K, Coutts A, Demuzere M, Dirks KN, Heaviside C, Lim S, Macintyre H (2016) Health and climate related ecosystem services provided by street trees in the urban environment. Environ Health 15:95.

The onset and end of wet seasons over Africa


For many Africans, the timing of the wet season is of crucial importance, especially for those reliant upon subsistence agriculture, who depend on the seasonal rains for crop irrigation. In addition, the wet season recharges lakes, rivers and water storage tanks which constitute the domestic water supply in some areas. The timing of the wet season also affects the availability of energy from hydroelectric schemes, and has impacts upon the prevalence of certain disease carrying vectors, such as mosquitoes.

Climate change is already threatening many vulnerable populations, and changes in the timing or intensity of the wet season, or increasing uncertainty in the timing of the onset, may lead to significant socio-economic impacts. But before we consider future projections or past changes in the seasonality, we need to go back a few steps.

The first step is to find a method for determining when the wet season starts and ends (its ‘onset’ and ‘cessation’). In order to look at large-scale shifts in the timing of the wet season and relate this to wider-scale drivers, this method needs to be applicable across the entirety of continental Africa. Most previous methods for determining the onset focus on the national to regional scale, and are dependent on the exceedance of a certain threshold e.g. the first week with at least 20mm of rainfall, with one rainfall event of more than 10mm, and no dry spell of more than 10 days after the rain event for the next month. While such definitions work well at a national scale they are not applicable at a continental scale where rainfall amounts vary substantially. A threshold suitable for the dry countries at the fringes of the Sahara would not be suitable in the wetter East African highlands.

In addition to a vast range of rainfall amounts, the African continent also spans multiple climatic regimes. The seasonal cycle of precipitation over continental Africa is largely driven by the seasonal progression of the ITCZ and associated rain belts, which follows the maximum incoming solar radiation. In the boreal summer, when the thermal equator sits between the equator and the Tropic of Cancer, the ITCZ sits north of the equator and West Africa and the Sahel experience a wet season. During the boreal autumn the ITCZ moves south, and southern Africa experiences a wet season during the austral summer, followed by the northward return of the ITCZ during the boreal spring. As a consequence of this, central African regions and the Horn of Africa experience two wet seasons per year – one as the ITCZ travels north, and a second as the ITCZ travels south. A method for determining the onset and cessation at the continental scale thus needs to account for regions with multiple wet seasons per year.

In our paper (available here) we propose such a method, based on the method of Liebmann et al (2012). The method has three steps:

  • Firstly, determine the number of seasons experienced per year at the location (or grid point) of interest. This is achieved using harmonic analysis – the amplitude of the first and second harmonic were computed, using the entire timeseries and their ratio compared. If the ratio was greater than 1.0, i.e. the amplitude of the second harmonic was greater than the amplitude of the first harmonic then the grid point was defined as having two wet seasons per year (biannual), if the ratio was less than one then it was defined as having an annual regime. Figure 1 shows the ratio for one African rainfall dataset (TARCATv2). Three regions are identified as biannual regions; the Horn of Africa, an equatorial strip extending from Gabon to Uganda and a small region on the southern West African coastline.

    Figure 1: Location of regions with one and two seasons per year, determined using harmonic analysis. Yellow indicates two seasons per year, while pink/purple indicates one season per year. Computed from TARCATv2 data.
  • Secondly the period of the year when the wet season occurs was determined. This was achieved by looking for minima and maxima in the climatological cumulative daily rainfall anomaly to identify one or two seasons.
  • The third and final stage is to calculate the onset and cessation dates for each year. This is done by looking for the minima and maxima in the cumulative daily rainfall anomaly, calculated for each season.

Figure 2 shows the seasonal progression of the onset and cessation, with the patterns observed in agreement with those expected from the driving physical mechanisms, and continuous progression across the annual/biannual boundaries. Over West Africa and the Sahel, Figure 2a-b shows zonally-contiguous progression patterns with onset following the onset of the long rains and moving north, and cessation moving southward, preceding the end of the short rains. Over southern Africa Figure 2c-d shows the onset over southern Africa starting in the north-west and south-east, following the onset of the short rains, reaching the East African coast last, and cessation starting at the Zimbabwe, Mozambique, South Africa border and spreading out radially into the cessation of the long rains.

As well as testing the method for compatibility with known physical drivers of African rainfall, agreement across multiple satellite-based rainfall estimates was also examined. In general, good agreement was found across the datasets, particularly for regions with an annual regime and over the biannual region of East Africa.

Figure 2: Southward and northward progression of the onset and cessation across the annual/biannual boundaries, computed using GPCP daily rainfall data 1998-2013.

The advantage of having a method that works at the continental scale is the ability to look at the impact of large-scale oscillations on wider-scale variability. One application of this method was to investigate the impact of El Niño upon both the annual rains and short rains (Figure 3). In Figure 3 we see the well-documented dipole in rainfall anomaly, with higher rainfall totals over 0–15°S and the Horn of Africa in El Niño years and the opposite between 15°S and 30°S.  This anomaly is stronger when we use this method compared with using standard meteorological seasons. We can also see that while the lower rainfall to the south is colocated with later onset dates and a consequentially shorter season, the higher rainfall over the Horn of Africa is associated with later cessation of the short rains, with only small differences in onset date.

Figure 3: a-c) Composite of onset, cessation and wet season rainfall in El Niño years for annual rains and short rains, minus the mean over 1982-2013, computed using CHIRPS data d) Oct-Feb rainfall anomaly in  years (CHIRPS).

In addition to using this method for research purposes, its application within an operational setting is also being explored. Hopefully, the method will be included within the Rainwatch platform, which will be able to provide users with a probabilistic estimate of whether or not the season has started, based on the rainfall experienced so far that year, and historical rainfall data.

For more details, please see the paper detailing this work:

Dunning, C.M., E Black, and R.P. Allan (2016) The onset and cessation of seasonal rainfall over Africa, Journal of Geophysical Research: Atmospheres, 121 11,405-11,424, doi: 10.1002/2016JD025428


Liebmann, B., I. Bladé, G. N. Kiladis, L. M. Carvalho, G. B. Senay, D. Allured, S. Leroux, and C. Funk (2012), Seasonality of African precipitation from 1996 to 2009, J. Clim.25(12), 4304–4322.

Can we really use El Niño to predict flooding?

R. Emerton, H. Cloke, E. Stephens, E. Zsoter, S. Woolnough, F. Pappenberger (2017). Complex picture for likelihood of ENSO-driven flood hazard. Nature Communications. doi: 10.1038/NCOMMS14796


When an El Niño is declared, or even forecast, we think back to memorable past El Niños (such as 1997/98), and begin to ask whether we will see the same impacts. Will California receive a lot of rainfall? Will we see droughts in tropical Asia and Australia? Will Peru experience the same devastating floods as in 1997/98, and 1982/83?


El Niño and La Niña, which see changes in the ocean temperatures in the tropical Pacific, are well known to affect weather, and indeed river flow and flooding, around the globe. But how well can we estimate the potential impacts of El Niño and La Niña, and how likely flooding is to occur?

This question is what some of us in the Water@Reading research group at the University of Reading have been looking to answer in our recent publication in Nature Communications. As part of our multi- and inter-disciplinary research, we work closely with the Red Cross / Red Crescent Climate Centre (RCCC), who are working on an initiative called Forecast-based Financing (FbF, Coughlan de Perez et al.). FbF aims to distribute aid (for example providing water purification tablets to prevent spread of disease, or digging trenches to divert flood water) ahead of a flood, based on forecasts. This approach helps to reduce the impact of the flood in the first place, rather than working to undo the damage once the flood has already occurred.

Photo credit: Red Cross / Red Crescent Climate Centre

In Peru, previous strong El Niños in 1982/83 and 1997/98 had resulted in devastating floods in several regions. As such, when forecasts in early 2015 began to indicate a very strong El Niño was developing, the RCCC and forecasters at the Peruvian national hydrological and meteorology agency (SENAMHI) began to look into the likelihood of flooding, and what FbF actions might need to be taken.

Typically, statistical products indicating the historical probability (likelihood [%] based on what happened during past El Niños) of extreme precipitation are used as a proxy for whether a region will experience flooding during an El Niño (or La Niña), such as these maps produced by the IRI (International Research Institute for Climate and Society). You may also have seen maps which circle regions of the globe that will be drier / warmer / wetter / cooler – we’ll come back to these shortly.

These rainfall maps show that Peru, alongside several other regions of the world, is likely to see more rainfall than usual during an El Niño. But does this necessarily mean there will be floods? And what products are out there indicating the effect of El Niño on rivers across the globe?

For organisations working at the global scale, such as the RCCC and other humanitarian aid agencies, global overviews of potential impacts are key in taking decisions on where to focus resources during an El Niño or La Niña. While these maps are useful for looking at the likely changes in precipitation, it has been shown that the link between precipitation and flood magnitude is nonlinear (Stephens et al.),  – more rain does not necessarily equal floods – so how does this transfer to the potential for flooding?

The motivation behind this work was to provide similar information, but taking into account the hydrology as well as the meteorology. We wanted to answer the question “what is the probability of flooding during El Niño?” not only for Peru, but for the global river network.

To do this, we have taken the new ECMWF ERA-20CM ensemble model reconstruction of the atmosphere, and run this through a hydrological model to produce the first 20th century global hydrological reconstruction of river flow. Using this new dataset, we have for the first time estimated the historical probability of increased or decreased flood hazard (defined as abnormally high or low river flow) during an El Niño (or La Niña), for the global river network.

Figure 1: The probability of increased (blue) or decreased (red) flood hazard during each month of an El Nino. Based on the ensemble mean of the ERA-20CM-R 20th century river flow reconstruction.

The question – “what is the probability of flooding during El Niño?”, however, remains difficult to answer. We now have maps of the probability of abnormally high or low river flow (see Figure 1), and we see clear differences between the hydrological analysis and precipitation. It is also evident that the probabilities themselves are often lower, and much more uncertain, than might be useful – how do you make a decision on whether to provide aid to an area worried about flooding, when the probability of that flooding is 50%?

Figure 2: Historical probability of increased / decreased flood hazard map for February, with overlay showing the typical impact map for winter during an El Nino. This highlights the complexity of the link between El Nino and flooding compared to the information usually available.

The likely impacts are much more complex than is often perceived and reported – going back to the afore-mentioned maps that circle regions of the globe and what their impact will be (warmer, drier, wetter?) – these maps portray these impacts as a certainty, not a probability, with the same impacts occurring across huge areas. For example, in Figure 2, we take one of the maps from our results, which indicates the probability of increased or decreased flood hazard in one month during an El Niño, and draw over this these oft-seen circles of potential impacts. In doing this, we remove all information on how likely (or unlikely) the impacts are, smaller scale changes within these circles (in some cases our flood hazard map even indicates a different impact), and a lot of the potential impacts outside of these circles – not to mention the likely impacts can change dramatically from one month to the next. For those organisations that take actions based on such information, it is important to be aware of the uncertainties surrounding the likely impacts of El Niño and La Niña.

“We conclude that while it may seem possible to use historical probabilities to evaluate regions across the globe that are more likely to be at risk of flooding during an El Niño / La Niña, and indeed circle large areas of the globe under one banner of wetter or drier, the reality is much more complex.”

PS. During the winter of 2015/16, our results estimated an ~80% likelihood of increased flood hazard in northern coastal Peru, with only ~10% uncertainty surrounding this. The RCCC took FbF actions to protect thousands of families from potentially devastating floods driven by one of the strongest El Niños on records. While flooding did occur, this was not as severe as expected based on the strength of the El Niño. More recently, during the past few months (January – March 2017), anomalously high sea surface temperatures (SSTs) in the far eastern Pacific (known as a “coastal El Niño” in Peru but not widely acknowledged as an El Niño because central Pacific SSTs are not anomalously warm) have led to devastating flooding in several regions and significant loss of life. And Peru wasn’t the only place that didn’t see the impacts it expected in 2015/16; other regions of the world, such as the US, also saw more rainfall than normal in places that were expected to be drier, and California didn’t receive the deluge they were perhaps hoping for. It’s important to remember that no two El Niños are the same, and El Niño will not be the only influence on the weather around the globe. While El Niño and La Niña can provide some added predictability to the atmosphere, the impacts are far from certain.

Presidente Kuczynski recorre zonas afectadas por lluvias e inund
Flooded areas of Trujillo, Peru, March 2017. Photo credit: Presidencia Peru, via Floodlist

Full reference:

R. Emerton, H. Cloke, E. Stephens, E. Zsoter, S. Woolnough, F. Pappenberger (2017). Complex picture for likelihood of ENSO-driven flood hazard. Nature Communications. doi: 10.1038/NCOMMS14796

Press Release:

Understanding our climate with tiny satellites

Gristey, J. J., J. C. Chiu, R. J. Gurney, S.-C. Han, and C. J. Morcrette (2017), Determination of global Earth outgoing radiation at high temporal resolution using a theoretical constellation of satellites, J. Geophys. Res. Atmos., 122, doi:10.1002/2016JD025514.

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The surface of our planet has warmed at an unprecedented rate since the mid-19th century and there is no sign that the rate of warming is slowing down. The last three decades have all been successively warmer than any preceding decade since 1850, and 16 of the 17 warmest years on record have all occurred since 2001. The latest science now tells us that it is extremely likely that human influence has been the dominant cause of the observed warming1, mainly due to the release of carbon dioxide and other greenhouse gases into our atmosphere. These greenhouse gases trap heat energy that would otherwise escape to space, which disrupts the balance of energy flows at the top of the atmosphere (Fig. 1). The current value of the resulting energy imbalance is approximately 0.6 W m–2, which is more than 17 times larger than all of the energy consumed by humans2! In fact, observing the changes in these energy flows at the top of the atmosphere can help us to gauge how much the Earth is likely to warm in the future and, perhaps more importantly, observations with sufficient spatial coverage, frequency and accuracy can help us to understand the processes that are causing this warming.

Figure 1. The Earth’s top-of-atmosphere energy budget. In equilibrium, the incoming sunlight is balanced by the reflected sunlight and emitted heat energy. Greenhouse gases can reduce the emitted heat energy by trapping heat in the Earth system leading to an energy imbalance at the top of the atmosphere.

Observations of energy flows at the top of the atmosphere have traditionally been made by large and expensive satellites that may be similar in size to a large car3, making it impractical to launch multiple satellites at once. Although such observations have led to many advancements in climate science, the fundamental sampling restrictions from a limited number of satellites makes it impossible to fully resolve the variability in the energy flows at the top of atmosphere. Only recently, due to advancements in small satellite technology and sensor miniaturisation, has a novel, viable and sustainable sampling strategy from a constellation of satellites become possible. Importantly, a constellation of small satellites (Fig. 2a), each the size of a shoe-box (Fig. 2b), could provide both the spatial coverage and frequency of sampling to properly resolve the top of atmosphere energy flows for the first time. Despite the promise of the constellation approach, its scientific potential for measuring energy flows at the top of the atmosphere has not been fully explored.

Figure 2. (a) A constellation of 36 small satellites orbiting the Earth. (b) One of the small “CubeSat” satellites hosting a miniaturised radiation sensor that could be used [edited from earthzine article].
To explore this potential, several experiments have been performed that simulate measurements from the theoretical constellation of satellites shown in Fig 2a. The results show that just 1 hour of measurements can be used to reconstruct accurate global maps of reflected sunlight and emitted heat energy (Fig. 3). These maps are reconstructed using a series of mathematical functions known as “spherical harmonics”, which extract the information from overlapping samples to enhance the spatial resolution by around a factor of 6 when compared with individual measurement footprints. After producing these maps every hour during one day, the uncertainty in the global-average hourly energy flows is 0.16 ± 0.45 W m–2 for reflected sunlight and 0.13 ± 0.15 W m–2 for emitted heat energy. Observations with these uncertainties would be capable of determining the sign of the 0.6 W m–2 energy imbalance directly from space4, even at very short timescales.

Figure 3. (top) “Truth” and (bottom) recovered enhanced-resolution maps of top of atmosphere energy flows for (left) reflected sunlight and (right) emitted heat energy, valid for 00-01 UTC on 29th August 2010.

Also investigated are potential issues that could restrict similar uncertainties being achieved in reality such as instrument calibration and a reduced number of satellites due to limited resources. Not surprisingly, the success of the approach will rely on calibration that ensures low systematic instrument biases, and on a sufficient number of satellites that ensures dense hourly sampling of the globe. Development and demonstration of miniaturised satellites and sensors is currently underway to ensure these criteria are met. Provided good calibration and sufficient satellites, this study demonstrates that the constellation concept would enable an unprecedented sampling capability and has a clear potential for improving observations of Earth’s energy flows.

This work was supported by the NERC SCENARIO DTP grant NE/L002566/1 and co-sponsored by the Met Office.


1 This statement is quoted from the latest Intergovernmental Panel on Climate Change assessment report. Note that these reports are produced approximately every 5 years and the statements concerning human influence on the climate have increased in confidence in every report.

2 Total energy consumed by humans in 2014 was 13805 Mtoe = 160552.15 TWh. This is an average power consumption of 160552.15 TWh  / 8760 hours in a year = 18.33 TW

Rate of energy imbalance per square metre at top of atmosphere is = 0.6 W m–2. Surface area of “top of atmosphere” at 80 km is 4 * pi * ((6371+80)*103 m)2 = 5.23*1014 m2. Rate of energy imbalance for entire Earth = 0.6 W m–2 * 5.23*1014 m2 = 3.14*1014 W = 314 TW

Multiples of energy consumed by humans = 314 TW / 18.33 TW = 17

3 The satellites currently carrying instruments that observe the top of atmosphere energy flows (eg. MeteoSat 8, Aqua) will typically also be hosting a suite of other instruments, which adds to the size of the satellite. However, even the individual instruments are still much larger that the satellite shown in Fig. 2b.

4 Currently, the single most accurate way to determine the top-of-atmosphere energy imbalance is to infer it from changes in ocean heat uptake. The reasoning is that the oceans contain over 90% of the heat capacity of the climate system, so it is assumed on multi-year time scales that excess energy accumulating at the top of the atmosphere goes into heating the oceans. The stated value of 0.6 W m–2 is calculated from a combination of ocean heat uptake and satellite observations.


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