Remodelling Building Design Sustainability from a Human Centred Approach (Refresh) project overview

By: Hannah Gough

In 2014, 54 % of the world’s population resided in an urban area and this is projected to rise to 66 % by 2050 (United Nations, 2014). It is also estimated that 90 % of people’s time in developed countries is spent indoors, either at home, at work, or travelling between the two (Klepeis et al., 2001). This equates to a lot of time spent indoors within an urban environment with the indoor and outdoor environments being strongly interlinked.

We’ve all experienced the after-lunch productivity slump, or wished to escape from a dark, stuffy and overcrowded meeting room. The Refresh project set out to explore the impact of urban microclimate on building ventilation for optimal performance of occupants using the meteorological knowledge from Reading, the indoor environmental expertise from Leeds and the human behaviour measurement skills from Southampton. The quality of indoor environments plays an important role in the physical and mental health and
well-being of the occupants (Vardoulakis and Heaviside, 2012).

Figure 1: a) Full-scale array at Silsoe UK, with scale marked in yellow, Car circled to give an indication of size. Red dot represents the reference mast, with the orange dot highlighting the location of the local mast. b) and c) are outputs of the CFD model. d) is the 20 mm cube used in the 1:300 scale wind tunnel model and e) is the cube from d) within the array. Refs: Gough et al., in review; King et al., 2017a; 2017b,  Gough, 2017; Gough et al., 2018a; Gough et al., 2018b

One part focused on flow behaviour in and around a building within a simplified urban environment through a full-scale field campaign which combined the methodologies of meteorology and engineering (Figure 1, Gough, 2017; Gough et al., 2018a; Gough et al., 2018b). The dataset spans nine months and is accompanied by wind tunnel (1:300 scale) experiments and CFD (Computational Fluid Dynamics) modelling to aid understanding (Figure 1, Gough et al., in review; King et al., 2017a; 2017b). So far, it’s been found that two methods of measuring ventilation (tracer gas and pressure difference) vary depending on the external driving conditions (Gough et al., 2018b) and that natural ventilation is difficult to predict due to the interaction of wind direction, wind speed, temperature and turbulence, sometimes causing a dual local flow behaviour for a single reference wind direction (Gough et al., 2018a). For the pressure on the cube faces, current models capture it well for a single building, but do not capture the correct shape for a surrounded building, due to the unique features of the site not being accounted for (Gough et al., in review). This will have a larger effect, especially in more built up urban areas.

Current work includes using the models created by the Dispersion of Air Pollution and its Penetration into the Local Environment (DAPPLE project)(Arnold et al., 2004; Dobre et al., 2005; Barlow et al., 2009) to predict local flow and testing existing models to predict natural ventilation rate against Refresh data (De Gids and Phaff, 1982; Warren and Parkins, 1985; Larsen et al., 2018).

Figure 2: Example of the Aether device highlighting the levels of CO2, temperature and humidity within a room with colour coded feedback for ease of understanding (Snow et al, in Prep)

Focusing on human behaviour within offices, poor indoor air quality does not always equate to rational actions by office workers to improve conditions (Snow et al., 2016). This means that although a building may operate perfectly in design tests, when you include people, you find that they may work against the design! Shared offices often have a social hierarchy of people, with ‘Gatekeepers’ for window opening or thermostat control. By including devices such as the Aether  (Snow et al, in prep) in the room indoor air quality is then socially negotiated.

Figure 3: Participant undergoing the calibration procedure of the EEG studies into the effect of CO2 within a typical university office environment. A CO2 sensor is visible on the drawers with cognitive performance tests being undertaken on the computer.

Tests using EEG (electroencephalogram) found a marginal effect of a 2,700 ppm CO2 environment (offices regularly reach this level) on executive function and the ability to sustain attention, regardless of the perception of the air quality (Snow et al., 2018) (Figure 3). This gives us the hypothesis that poor indoor air quality can impact cognitive performance prior to individual awareness.

Looking forwards, we’re going to be working with the MAGIC project at their field-site in London (Figure 4) using Doppler lidar wind data and looking into the benefits of post-occupancy evaluations.

Arnold, S.J., ApSimon, H., Barlow, J., Belcher, S., Bell, M., Boddy, J.W., Britter, R., Cheng, H., Clark, R., Colvile, R.N., Dimitroulopoulou, S., Dobre, a, Greally, B., Kaur, S., Knights, a, Lawton, T., Makepeace, a, Martin, D., Neophytou, M., Neville, S., Nieuwenhuijsen, M., Nickless, G., Price, C., Robins, a, Shallcross, D., Simmonds, P., Smalley, R.J., Tate, J., Tomlin, a S., Wang, H., Walsh, P., 2004. Introduction to the DAPPLE Air Pollution Project. Sci. Total Environ. 332, 139–53. doi:10.1016/j.scitotenv.2004.04.020

Barlow, J.F., Dobre, A., Smalley, R.J., Arnold, S.J., Tomlin, A.S., Belcher, S.E., 2009. Referencing of street-level flows measured during the DAPPLE 2004 campaign. Atmos. Environ. 43, 5536–5544. doi:10.1016/j.atmosenv.2009.05.021

De Gids, W., Phaff, H., 1982. Ventilation rates and energy consumption due to open windows: a brief overview of research in the Netherlands. Air infiltration Rev. 4, 4–5.

Dobre, A., Arnold, S., Smalley, R., Boddy, J., Barlow, J., Tomlin, A., Belcher, S., 2005. Flow field measurements in the proximity of an urban intersection in London, UK. Atmos. Environ. 39, 4647–4657. doi:10.1016/j.atmosenv.2005.04.015

Gough, H., 2017. Effects of meteorological conditions on building natural ventilation in idealised urban settings. PhD thesis. University of Reading, Department of Meteorology.
Gough, H., Sato, T., Halios, C., Grimmond, C.S.B., Luo, Z., Barlow, J.F., Robertson, A., Hoxey, A., Quinn, A., 2018. Effects of variability of local winds on cross ventilation for a simplified building within a full-scale asymmetric array: Overview of the Silsoe field campaign. J. Wind Eng. Ind. Aerodyn. 175C, 408–418.

Gough, H.L., King, M.-F., Nathan, P., Sue Grimmond, C.S., Robins, A.G., Noakes, C.J., Luo, Z., Barlow, J.F., n.d. Influence of neighbouring structures on building façade pressures: comparison between full-scale, wind-tunnel, CFD and practitioner guidelines. J. Wind Eng. Ind. Aerodyn.

Gough, H.L., Luo, Z., Halios, C.H., King, M.F., Noakes, C.J., Grimmond, C.S.B., Barlow, J.F., Hoxey, R., Quinn, A.D., 2018. Field measurement of natural ventilation rate in an idealised full-scale building located in a staggered urban array: Comparison between tracer gas and pressure-based methods. Build. Environ. 137, 246–256. doi:10.1016/j.buildenv.2018.03.055

King, M.F., Gough, H.L., Halios, C., Barlow, J.F., Robertson, A., Hoxey, R., Noakes, C.J., 2017a. Investigating the influence of neighbouring structures on natural ventilation potential of a full-scale cubical building using time-dependent CFD. J. Wind Eng. Ind. Aerodyn. 169, 265–279. doi:10.1016/j.jweia.2017.07.020

King, M.F., Khan, A., Delbosc, N., Gough, H.L., Halios, C., Barlow, J.F., Noakes, C.J., 2017b. Modelling urban airflow and natural ventilation using a GPU-based lattice-Boltzmann method. Build. Environ. 125, 273–284. doi:10.1016/j.buildenv.2017.08.048

Klepeis, N.E., Nelson, W.C., Ott, W.R., Robinson, J.P., Tsang, A.M., Switzer, P., Behar, J. V, Hern, S.C., Engelmann, W.H., 2001. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J. Expo. Sci. Environ. Epidemiol. 11, 231.

Larsen, T.S., Plesner, C., Leprince, V., Carrié, F.R., Bejder, A.K., 2018. Calculation methods for single-sided natural ventilation: Now and ahead. Energy Build. 177, 279–289. doi:10.1016/j.enbuild.2018.06.047

Snow, S., Boyson, A., King, M.-F., Malik, O., Coutts, L., Noakes, C., Gough, H., Barlow, J., Schraefel, M. c., 2018. Using EEG to characterise drowsiness during short duration exposure to elevated indoor Carbon Dioxide concentrations. bioRxiv 483750. doi:10.1101/483750

Snow, S., Soska, A., Chatterjee, S.K., Schraefel, M. c., 2016. Keep Calm and Carry On: Exploring the Social Determinants of Indoor Environment Quality, in: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems – CHI EA ’16. ACM Press, New York, New York, USA, pp. 1476–1482. doi:10.1145/2851581.2892490

United Nations, 2014. World Urbanization Prospects 2014 revision (highlights). New York.

Vardoulakis, S., Heaviside, C., 2012. Health Effects of Climate Change in the UK 2012.

Warren, P.R., Parkins, L.M., 1985. Single-sided ventilation through open windows, in: Conf. Proc. Thermal Performance of the Exterior Envelopes of Buildings, ASHRAE, Florida. p. 20.


Posted in Boundary layer, Climate, Urban meteorology | Leave a comment

A new early warning and decision support system: TAMSAT-ALERT

By Emily Black

A new early warning and decision support system: TAMSAT-ALERT 
For subsistence farmers in Africa, decisions on what variety of crop to grow, when to plant, and when to apply fertilizer are of life and death importance. The new TAMSAT* Agricultural Early Warning System (TAMSAT-ALERT) combines multiple streams of environmental data into probabilistic assessments of the risk faced by farmers when making these decisions. The assessments can be issued directly to farmers to support day-to-day decision making, or provided via drought warning bulletins from regional meteorological and agricultural organisations.

TAMSAT-ALERT risk assessments can be based on any metric that can be generated from meteorological data. Our work has so far focused on meteorological and agricultural drought – encapsulated respectively by deficit in cumulative rainfall and soil moisture.

Figure 1: Predicted cumulative rainfall anomaly for a location in Kenya for the 2000—2001 DJF rainy season. The forecast was carried out on 10th Decemeber 2000.

Figure 2: Predicted soil moisture anomaly (left) and Water resource Satisfaction Index (right) for the 2017 October-December rainy season in Kenya. This prediction was made on 1st December, 2017.

TAMSAT-ALERT complements existing systems because it is sufficiently lightweight to be run on a standard PC, using the computing facilities available at African meteorological and hydrological services . Example outputs are shown in Figure 1 and 2. The system can be implemented both for detailed analysis at the individual community scale (Figure 1) and for regional/national level assessments (Figure 2). The national forecast shown in Figure 2, for example, took only ten minutes to generate. All the code for TAMSAT-ALERT is freely available on GitHub , and TAMSAT is working closely with African organisations to build their capacity to use the system.

Figure 3: A screen shot of the TAMSAT-ALERT web interface

For less expert users, we are developing a web interface, which will provide a limited set of output plots for any point in Africa (Figure 3).

Figure 4: TAMSAT-ALERT example output. The ‘true’ historical (thick blue lines) and projected soil moisture in northern Ghana (thin red lines), left, for 2011 (top) and 2003 (bottom) and the corresponding evolving drought probability (right), where the vertical black lines represent the time steps of the three left plots. The drought probability is probability that the projected soil moisture for the growing season will be in the lowest quartile of climatology (from Brown et al, 2017)

The science behind TAMSAT-ALERT

Agricultural outcomes are affected by weather over an extended period, ranging from days to months. For example, low yield is caused by soil moisture deficit at critical periods during a ~ three month growing season; germination failure is caused by low rainfall during the two weeks after planting. The risk assessment on a given day, therefore, needs to take into account weather in the past and the future. In TAMSAT-ALERT, weather in the past is taken from observations, and an ensemble of future weather is derived from the climatology. Meteorological forecast information is integrated into the assessments by weighting the ensemble using probabilistic output from a numerical forecast model. This process is illustrated by Figure 4 and by the example above.

An example…
A prediction of soil moisture deficit for the 2018 March-May growing season carried out on 1st April, will be created by driving a land surface model, such as the UK land surface model, JULES, with observations from 1st March – 1st April 2018 spliced together with data a 2nd April – 30th May climatology. If the climatology is from 1983-2012, the ensemble will have 30 members. Ensemble member 1 will be yield output from the model driven with historical observations for 1st March – 1st April 2018, spliced with historical observations from 2nd April – 30th May 1983. The second member will use 2nd April – 30th May 1984 for the future period, and so on.

The result will be 30 possible predictions of soil moisture – a frequency distribution.

The next step is to integrate meteorological forecasts. In this year (2018), we have a prediction that the probability of regional MAM rainfall being in the lowest tercile is 60%, the middle tercile is 30% and the upper tercile is 10%. For example, if in 1983, regional rainfall was in the lowest tercile, the 1983 ensemble member is weighted by 0.6; if in 1984, rainfall is weighted by 0.1, and so on. Probabilistic risk assessments are then derived by analysing this weighted frequency distribution.

In essence, the system quantitatively addresses the question: ‘Given the state of the land surface, the climatology and the meteorological/climate forecast, what is the likelihood of some adverse food production event over the coming cropping period?’ As such, TAMSAT-ALERT is an ‘impacts-based’ forecast system, providing information aligned with the needs of operational food security risk assessments.

TAMSAT-ALERT is run at the scale of the input meteorological observational data. It thus implicitly downscales and bias corrects regional seasonal forecast data. The system has recently been extended to run in gridded mode. Pilot projects confirm that the system is sufficiently lightweight to run in African agrometeorological agencies.

*TAMSAT stands for Tropical Applications of Meteorology using SATellite data and ground-based observations.

Asfaw, D., Black, E., Brown, M., Nicklin, K.J., Otu-Larbi, F., Pinnington, E., Challinor, A., Maidment, R. and Quaife, T., 2018. TAMSAT-ALERT v1: A new framework for agricultural decision support. Geoscientific Model Development, 11(6), pp.2353-2371. DOI: 10.5194/gmd-11-2353-2018

Brown, M., Black, E., Asfaw, D. and Otu‐Larbi, F., 2017. Monitoring drought in Ghana using TAMSAT‐ALERT: a new decision support system. Weather, 72(7), pp.201-205. DOI: 10.1002/wea.3033


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Laboratory experiments investigating falling snowflakes

By: Mark McCorquodale

In the UK there are, on average, just 23.7 days of snowfall or sleet a year. However, precipitation in the form of ice crystals, or snowflakes, is an important feature within the atmosphere, both in the UK and worldwide. Research indicates that 50% of global precipitation events are linked to the production of ice in clouds, either falling as snow or melting as it falls to produce rain1. This percentage increases to 85% of precipitation events in mid-latitudes (including the UK) and 98% of precipitation events in Polar regions. The small ice particles formed in clouds precipitate slowly, at a much smaller velocity than large snowflakes falling in the lower atmosphere. However, this process must be represented within climate models as the precipitation of ice crystals determines the lifetime of clouds, which in turn impacts the atmosphere’s energy balance by, for example, reflecting incoming radiation from the sun.

Unfortunately, ice crystals are complex 3D structures and we know very little about the aerodynamics of these particles since it is essentially impossible to study the precipitation of snowflakes as they fall through the atmosphere. Even studying snowfall at the Earth’s surface is challenging; natural snowflakes are small in size and have a tendency to break or melt when handled. Consequently, despite extensive field observation studies, many aspects of the aerodynamics of snowflakes are poorly understand due to a lack of detailed experimental data. New data on the aerodynamics of complex snowflakes is required to inform future microphysics schemes and remote-sensing algorithms that are used for weather prediction and climate modelling.

Figure 1: Digital models of ice crystals commonly observed in different atmospheric conditions. (a-c) Planar crystals, of various forms, are prevalent at temperatures in the region of -15oC, (d) bullet rosettes are prevalent at temperatures in the region of -40oC, (e,f) aggregates of ice crystals form under a range of conditions, including in deep cirrus clouds and snowstorms.

To overcome these challenges, the Remote Sensing and Clouds research group have adopted a novel laboratory-based approach, whereby 3D-printers are used to fabricate models of natural ice crystals. Having developed a “library” of typical snowflake shapes2, which form under different atmospheric conditions, the free-fall of 3D-printed snowflake analogues can be studied under controlled laboratory conditions. Examples of snowflake analogues used are shown in figure 1. Critically, this approach enables us to study complex ice crystal geometries, such as aggregates of ice crystals, for which comparable data is not otherwise available. Early work on this project3 provided data that support an existing model of the drag force that acts on falling snowflakes (described through a drag coefficient), which can be used to estimate terminal velocities of natural snowflakes.

Figure 2: Digital reconstructions of the trajectory of falling snowflake analogues; a series of superimposed “snapshots” of each particle in free-fall are shown which illustrate the relative motion and orientation of the models. (a) Planar snowflake analogues with low mass (low Reynolds number) exhibit a steady free-fall with a stable orientation. (b) Planar snowflake analogues of greater mass (increasing Reynolds number) exhibit unsteady motions as they fall. (c,d) Aggregate snowflake analogues with an irregular geometry are routinely observed to rotate as they fall in a spiralling motion.  

Recently we have developed an experimental approach that enables the trajectory of the falling snowflake analogues to be digitally reconstructed by using images from a series of synchronised digital cameras. This new approach enables us to acquire more comprehensive data on the free-fall of snowflake analogues. In particular, these data enable us to quantify the orientation of the snowflake analogues and the unstable motions that they exhibit as they fall – typical examples are shown in Figure 2. These data will enable us to investigate the parameters that control the orientation of complex ice crystals and to improve models of the drag force that acts on ice crystals (in order to better estimate the terminal velocity of natural snowflakes with complex geometries). We hope to submit detailed results for publication later in 2019. 


  1. Field, P. R., Heymsfield, A. J. 2015: Importance of snow to global precipitation. Geophys. Res. Lett.,42,9512–9520,
  2. Kikuchi, K., Kameda, T., Higuchi, K., Yamashita, A. 2013 A global classification of snow crystals, ice crystals, and solid precipitation based on observation from middle latitudes to Polar Regions. Atmos. Res., 132:460-472. DOI: 10.1016/j.atmosres.2013.06.006.
  3. Westbrook, C. D. Sephton, E. K. 2017 Using 3-D-printed analogues to investigate the fall speeds and orientations of complex ice particles. Geophys.  Res. Lett., 44, DOI: 10.1002/2017GL074130.
Posted in Microphysics | Leave a comment

ESMValTool : An Evaluation Tool for Earth System Models

By: Ranjini Swaminathan

Global Climate Models are computer programs that simulate the atmosphere, ocean, sea ice, land surface, and the effects of these components on one another. Earth System Models (ESMs) are global climate models that include biogeochemical cycling in terrestrial and marine ecosystems and explicitly model the movement of carbon through the earth system. Output from ESMs are estimates of variables such as precipitation and temperature at different spatial and temporal resolutions and are typically available as three dimensional gridded values.

The Coupled Model Intercomparison Project (CMIP), now in its sixth phase (CMIP6) provides the climate and impact assessment community with a wide range of simulation output from Earth System Models (Eyring et al., 2016b). These simulations help us better understand past climate and also provide estimates for the future under different climate scenarios. It is therefore important to evaluate model simulation output in order to better understand systemic biases in the models and the magnitude of uncertainty in estimates of future projections before extensively using them for analyses and impact assessment studies. There is also a need for a comprehensive framework that performs baseline aspects of model evaluation in a consistent and efficient manner. This will prevent scientists wasting valuable time and effort individually re-implementing well-established evaluation diagnostics instead of just sharing them. A tool or framework that implements basic metrics and diagnostics for model output evaluation with the flexibility of adding newly-researched,  science-based evaluation measures is therefore the need of the hour. We present the Earth System Model Evaluation Tool (ESMValTool) as a tool compatible with current and future phases of the CMIP and many desirable features for comprehensive model evaluation (Eyring et al., 2016a).

Figure 1: Overview and basic working of the ESMValTool

The ESMValTool  is an open-source community diagnostics and performance metrics tool for the evaluation of ESMs. It allows comparisons between multiple models against previous versions and observations. The tool provides a versatile preprocessor to standardize data with regridding, interpolation and masking as well as temporal and spatial extraction facilities. Standard recipes for scientific topics reproduce specific diagnostics and performance metrics considered important for ESM evaluation from peer-reviewed literature (Lauer et al., 2017). The tool is currently at version 2.0 and the most recent release allows new analyses and recipes to be written in one of many open-source languages such as Python, R and NCL. Figure 1 shows a high-level overview of the ESMValTool.

Some key features of the tool are:

  1. Simultaneous multimodel comparison with observed data. Figure 2 shows an example of seasonal standard deviations of surface temperature anomalies for four different CMIP5 models and observed data as generated by the ESMValTool. We see that three of the models show a greater variability than observed data.
  2. Extensive documentation with log files for better traceability and reproducibility.
  3. Wide scope with standard recipes covering different aspects of ESMs such as dynamics, radiation, aerosols and sea ice.
  4. Central installation on the Centre for Environmental Data Analysis’s (CEDA’s) JASMIN infrastructure with CMIP model data retrieval being automatically facilitated.
  5. Diagnostic outputs produced in convenient formats such as plots (pdf, png etc.) and netCDF files.

The ESMValTool v2.0 continues to evolve with diagnostics being ported from the previous release, more relevant diagnostics ported from other assessment tools as well as new ones being added. The tool can be accessed from the ESMValTool GitHub page and a sample of CMIP results produced with the ESMValTool is available for the German CMIP6 project. We encourage users to access the tool and try out the various diagnostics and metrics available.

Figure 2: Standard deviation of global surface temperature anomalies for the period 1982-2005. Figures (a) through (d) are  for different CMIP5 climate model temperature outputs and (e) shows the standard deviation for observations (in this case, from HadCRUT4). Missing data in the observations is represented in white.


Eyring et al. 2016a Towards improved and more routine Earth System Model Evaluation in CMIP5. Earth System Dynamics, 7, 813-830, http://doi:10.5194/esd-7-813-2016

Lauer et al. 2017 Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool, Remote Sensing Environment, 203, 9-39, http://doi:10.1016/j.rse.2017.01.007

The ESMValTool Webpage:,

Eyring et al. 2016b Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geoscientific Model Development, 9, 1937-1958, 

ESMValTool GitHub Page:

German CMIP6 Project evaluation results with the ESMValTool:  


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North Atlantic post-tropical cyclones

By Alexander Baker

Figure 1: The 2017 North Atlantic hurricane season. Ophelia’s location stands out from the typical tracks of North Atlantic tropical cyclones during the active 2017 season. Selected major hurricanes occurring during 2017 – Irma, Jose and Maria – are labelled. Track data are from National Hurricane Center. (Figure courtesy of Jo Camp, Met Office.)

In the early hours of the 16th October 2017, an unusual storm made landfall in Ireland and continued to track north-eastwards, causing damage across the northern United Kingdom and Scandinavia over a two-day period. That storm – Ex-hurricane Ophelia – was the easternmost major hurricane (i.e., category 3 or higher) observed in the Atlantic during the satellite era (Figure 1). Ophelia had a tropical origin and structure and transitioned to an extratropical cyclone prior to landfall. It was not the first such ‘post-tropical’ Atlantic storm to make landfall in the mid-latitudes. To give just a few examples, Hurricane Lili (1996) hit the UK and Hurricanes Vince (2005) and Leslie (2018) struck the Iberian Peninsula. ‘Superstorm’ Sandy (2012) – the 4th most costly Atlantic hurricane on record – caused widespread damage across the Northeast United States.

Figure 2: Interannual variability in North Atlantic post-tropical cyclones. (left) Annual total post-tropical cyclone count timeseries for Europe (upper panel) and the Northeast US (lower panel) and (right) non-parametric (Spearman’s rho) inter-reanalysis correlation matrices (significant correlations, where p < 0.01, are in bold type).

Post-tropical cyclones expose populous mid-latitude communities, which may lack the preparedness of regions more commonly struck by tropical storms, to hurricane-force wind speeds and extreme precipitation. The risk of such events is projected to increase in response to anthropogenic climate change because warming may induce poleward and eastward expansion of tropical cyclone genesis areas, allowing more storms to propagate to the mid-latitudes, where they may undergo extratropical transition and re-intensify (Haarsma et al., 2013). A quantitative survey of historical post-tropical cyclone variability – against which climate model fidelity may be evaluated and climate change projections put into context – is therefore required.

To understand historical post-tropical cyclone activity across the North Atlantic, we used an objective storm-tracking algorithm (Hodges, 1995) to identify post-tropical cyclones in four reanalysis datasets (ERA-Interim, JRA-55, MERRA2 and NCEP-CFSR). Here, we briefly discuss interannual variability in post-tropical cyclones, their intensity prior to and following landfall, and their associated precipitation, focussing on those systems which impact Europe and the Northeast US.

So, what have we found? Let us first consider year-to-year variability. The reanalyses show no significant trends in total annual count of post-tropical cyclones impacting Europe or the Northeast US since 1979, although pronounced interannual variability exists (Figure 2, left). Overall, Europe and the Northeast US experience around five and ten post-tropical cyclones per year, respectively, including relatively weak systems. Interestingly, highly active Atlantic hurricane seasons, such as 2005 (the most active ever recorded), do not stand out as peaks in post-tropical cyclone activity. Moreover, there is little co-variability between Europe and the US: that is to say, peaks/troughs in Northeast US activity generally do not correspond with peaks/troughs in Europe. Inter-reanalysis correlations are higher for Europe than for the US (Figure 2, right), suggesting that the different model configurations and resolutions as well as the differing observational data assimilation schemes employed by the reanalyses impact the representation of historical post-tropical cyclone activity over the Northeast US more than that over Europe.

Figure 3: Composite post-tropical cyclone lifecycles. Lifecycles of difference post-tropical cyclone types centred (i.e., where t=0) on landfall within the Northeast US. Legend entries: ‘sym’/‘asym’ = symmetrical/asymmetrical; ‘WC’/‘CC’ = warm-core/cold-core. Legend numbers give the number of cyclones in each Hart category. Shading shows one standard deviation. Example results for the Northeast US from JRA-55.

We now turn briefly to storm structure and the two primary hazards posed by storms: wind and precipitation.

Hart (2003) devised a way to describe cyclone structure and its evolution based on three parameters: cyclone symmetry and lower- and upper-level thermal winds. These parameters allow cyclones to be classified as warm- or cold-core and symmetrical or asymmetrical (frontal). Typically, tropical cyclones are symmetrical and warm-core; extratropical cyclones are asymmetrical and cold-core. Post-tropical cyclones exhibit these – as well as hybrid – structures. By classifying post-tropical cyclones in this way, we found that the majority transition to a typical extratropical structure, but an appreciable number retain structural aspects of their tropical origins (Figure 3). Crucially, warm-core post-tropical cyclones possess the highest wind speeds upon landfall.

Figure 4: Post-tropical contributions to total precipitation. Average post-tropical cyclone precipitation contributions during August for the Northeast US (left) and Europe (right). TRMM precipitation observations are available up to 50°N and the black boxes outline the cyclone landfall domains within the domain of TRMM observational coverage (although both landfall domains extend north of this latitude).

To establish the importance of post-tropical cyclones are for precipitation over Europe and the Northeast US, we sampled satellite-based precipitation estimates (from NASA Tropical Rainfall Measuring Mission) in the vicinity of each cyclone track and calculated post-tropical cyclones’ percentage contribution to the total precipitation. These contributions were mapped across our regions of interest (Figure 4). Post-tropical cyclones are responsible for up to 10% of Europe’s total summer precipitation. This is significant given that, on average, only a few such storms impact southern Europe and the Mediterranean each summer (Figure 2). Across the US East Coast, climatological contributions of up to 50% are seen in August, and contributions of up to 20% occur across New England and parts of southern Atlantic Canada. Collectively, these results highlight the contemporary importance of post-tropical cyclone precipitation.

This work is currently in preparation for publication (Baker et al., 2018). We hope these analyses stimulate further discussion of post-tropical systems, particularly their tracks, tropical-to-extratropical transition, and representation in global climate models. Future work within the PRIMAVERA project will (i) perform comparisons with multiple high-resolution, tropical-cyclone-permitting global climate model simulations and (ii) assess the spread of post-tropical storm risk across the mid-latitudes under anthropogenic climate change.

Finally, an exciting PhD opportunity in the Department of Meteorology, exploring this topic is currently open for applications, and more information is available here.


Baker, A. J., Hodges, K., Schiemann, S., Haarsma, R., and Vidale, P. L. Historical variability of North Atlantic post-tropical cyclones and their importance for extratropical precipitation. (in prep.)

Haarsma, R. J., Hazeleger, W., Severijns, C., Vries, H., Sterl, A., Bintanja, R., Oldenborgh, G. J., and Brink, H. W., 2013. More hurricanes to hit western Europe due to global warming. Geophysical Research Letters 40, 1783-1788.

Hart, R. E., 2003. A Cyclone Phase Space Derived from Thermal Wind and Thermal  Asymmetry. Monthly Weather Review 131, 585-616.<0585:ACPSDF>2.0.CO;2 

Hodges, K. I., 1995. Feature Tracking on the Unit Sphere. Monthly Weather Review 123, 3458-3465.<3458:FTOTUS>2.0.CO;2 


I thank the EU Horizon-2020-funded PRIMAVERA project for financial support of this research and colleagues at the Royal Netherlands Meteorological Institute (KNMI) for helpful feedback.

Posted in Climate, Climate change, Climate modelling, extratropical cyclones, Rainfall, Tropical cyclones | Leave a comment

Later Wet Seasons with More Intense Rainfall over Africa under Future Climate Change

By Caroline Dunning

Rainfall is projected to change as the planet warms in response to rising concentrations of atmospheric greenhouse gases. New research indicates future changes in the timing and characteristics of the rainy seasons over Africa with important implications for impacts of climate change on vulnerable societies.

The fourth IPCC (Intergovernmental Panel on Climate Change) Assessment Report (AR4) states that “Africa is one of the most vulnerable continents to climate change and climate variability”1. This is partly related to low capacity for adaptation across much of the continent but is also related to the high dependence on climate, in particular wet season rainfall.

Most of Africa experiences one or two main wet seasons per year, when the majority of the annual rainfall occurs. For the large proportion of the population dependent upon rain-fed agriculture for their income and subsistence, the timing of this wet season and amount of rainfall is of high importance. Furthermore, wet season rainfall also impacts the recharge of reservoirs, the supply of electricity from hydro-power and the lifecycle of vectors (e.g. mosquitoes) responsible for the transmission of diseases such as malaria. It is therefore important to understand how future climate change will affect wet season rainfall across Africa.

Figure 1: Schematic showing changes in timing of the wet season (left) and changes in seasonal rainfall totals (right).

In a recently published paper, we use a novel method to investigate changes in the seasonal progression of rainfall across Africa. This methodology determines the start (‘onset’) and end (‘cessation’) of the wet season and is applicable across continental Africa. By applying this methodology to climate model simulations (RCP 4.5 and RCP 8.5 scenarios), we produced projections of changes in wet season onset, cessation, length and rainfall totals under future climate change (Figure 1).

The projections show a delay in the onset of the wet season across much of western and southern Africa under future climate change. In regions where there is little change in the end date (‘cessation’, e.g. western Africa) or regions where the end of the wet season gets earlier (e.g. southern Africa) a later onset means the wet season shortens. This may be problematic for crops, as a shorter wet season can lead to a shorter growing season and result in crops not reaching full maturity.

Across much of central Africa projections suggest that the amount of rainfall occurring during the wet season will increase. However, over southern Africa projections show lower rainfall totals. In such regions agricultural adaptation (e.g. to alternative crop varieties) may be required.

The Horn of Africa (Somalia, southern Ethiopia, Kenya and Uganda) and equatorial regions experience two wet seasons per year; one in the Northern Hemisphere spring (known as the long rains) and one in the autumn (known as the short rains). Our results show the long rains ending earlier, and the short rains ending later. The most notable result, however, is the large increase in the amount of rainfall occurring during the short rains.

We linked these changes in timing of the progression of the seasonal rainfall with changes in the summer heat low over the Saharan Desert (see paper for full details).

Another important finding is that within the wet season rainfall intensity will increase, whilst frequency will decrease; heavier rainfall is damaging to crops with delicate flowers such as coffee and cocoa. In addition, long dry periods can reduce soil moisture and harden the surface layer; thus, when heavy rainfall events do occur a smaller fraction infiltrates into the soil and increased runoff leads to soil erosion and flooding.

Overall, we found that future climate change will lead to changes in the timing of wet seasons over Africa, with shorter wet seasons over southern Africa. Seasonal rainfall totals are projected to increase over central regions, but decrease over southern Africa.  Suitable adaptation may be required; for example, using crop varieties that can cope with a shorter growing season or altering farming practices to minimise soil erosion. Other adaptation may be required to protect crops from increasing intensity of rainfall.


Dunning, C.M., Black, E., Allan, R.P., 2018: Later Wet Seasons with More Intense Rainfall over Africa under Future Climate Change. J. Climate, 31, 9719–9738, 

Posted in Africa, Climate, Climate change, Climate modelling, Rainfall | Leave a comment

Weather, Climate and Energy

By David Brayshaw

 A secure, reliable and relatively affordable electricity supply is an essential part of modern life in many parts of the world.  When charging an iPad, for example, one expects that power will be available at the flick of a switch and rarely considers the complex chain of events involved in producing and conveying the electricity to the device.

The production of energy – including electricity – accounts for a large part of national and global greenhouse gas emissions.  Decarbonizing the energy sector is therefore a key ingredient in meeting climate change targets both in the UK and worldwide.  Electricity – widely seen as the easiest form of energy to decarbonize – has seen a rapid shift towards renewables, particularly wind and solar photovoltaic (PV).

In a national-scale electricity system it is difficult to store large volumes of power efficiently and as such a near-instantaneous balance between supply and demand must be maintained.  Thuswhile the growing use of weather-sensitive renewable generation is clearly beneficial in terms of reducing carbon emissions, it presents new challenges for electricity system management as electricity production cannot be controlled to the same extent as that from, for example a traditional gas-fired power station. 

Figure 1: Simulated changes in weather sensitivity of the electricity system in Great Britain as increasing amounts of wind power capacity is installed.  The red line shows the strength of the negative relationship between temperature and annual total demand-net-wind (primarily associated with cold periods driving high demand), which decreases as installed wind capacity increases.  The blue line, in contrast, shows the increasing strength of the relationship between wind-speed and demand-net-wind as installed wind capacity increases (the sign is also negative because more wind implies a lower demand-net-wind).  Recent installed wind capacities for the GB electricity system are marked in grey lines, suggesting that the system’s dominant weather sensitivity changed from being temperature to wind sometime between 2009 and 2015.  The analysis is based on a 36-year modelled “reconstruction” of the power system’s behaviour derived from weather records.  Adapted from Bloomfield et al. (2018)1

This can be illustrated with an example.  It has long been known that electricity demand is strongly sensitive to temperature: as temperatures fall in winter, electricity demand rises.  Recent research, however, suggests that in the UK the impact of year-to-year variations in temperature via demand are now typically smaller than year-to-year variations in wind power supply (as shown in the figure 1).  That is, in terms of the amount of electricity that must be supplied by coal, gas and nuclear power stations, the impact of a calm winter now exceeds a cold winter1 while winter-time weather patterns associated with both cold and still conditions lead to a double whammy (both high demand and low supply2,3,4).  This suggest an important but subtle shift in how the weather risk should be viewed.  To assess how much non-renewable generation capacity is required, it makes more sense to ask what the maximum demand-net-renewables is (i.e., the demand remaining once the contribution from renewables is removed).  Such a calculation is easier than quantifying the minimum renewable generation available during times of peak demand (the so called “capacity credit” of renewables).

The need for high-quality tools for assessing and understanding the impact of weather and a changing climate on the energy system has never been greater.  The Energy-Meteorology group5, working closely with its industrial and academic partners, is leading the development of many such tools: from the first use of multi-decadal “energy system data reconstructions”6, to the development of new European climate services for energy (weeks-to-months ahead7,8,9), and the exploitation of state-of-the-art high-resolution climate data for informing the design of climate-resilient power systems in the coming decade10,11.


  1. Bloomfield, H. C., Brayshaw, D. J., Shaffrey, L. C., Coker, P. J., Thornton H. E., 2018: The changing sensitivity of power systems to meteorological drivers: a case study of Great Britain. Res. Lett., 13 (5), 054028,
  2. Thornton, H. E., Scaife, A. A., Hoskins, B. J., Brayshaw, D. J., 2017: The relationship between wind power, electricity demand and winter weather patterns in Great Britain. Res. Lett., 12 (6), 064017,
  3. Ely, C. R, Brayshaw, D. J., Methven, J., Cox, J., Pearce, O., 2013: Implications of the North Atlantic Oscillation for a UK–Norway renewable power system. Energy Policy, 62, 1420-1427,
  4. Brayshaw, D. J., Dent, C., Zacharay, S., 2012: Wind generation’s contribution to supporting peak electricity demand: meteorological insights. Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 226 (1), 44-50,
  5. University of Reading, 2018: Research Themes – Energy Meteorology. Accessed 6 December 2018,
  6. Cannon, D. J., Brayshaw, D. J., Methven, J., Coker, P. J., Lenaghan, D., 2015: Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renewable Energy, 75, 767-778,
  7. Thornton, H. E., Scaife, A., Hoskins, B. J., Brayshaw, D. J., Smith, D., Dunstone, N., Stringer, N., Bett, P. E., 2018: Skilful seasonal prediction of winter gas demand. Environmental Research Letters. Res. Lett., (in press).
  8. C3S, 2018: European Climatic Energy Mixes. Accessed 6 December 2018,
  9. Earth System Services, 2018: S2S4E Climate Services for Clean Energy. Accessed 6 December 2018,
  10. Santos-Alamillos, F. J, Brayshaw, D. J., Methven, J., Thomaidis, N. S., Ruiz-Arias, J. A., Pozo-Vázquez, D., 2017: Exploring the meteorological potential for planning a high performance European Electricity Super-grid: optimal power capacity distribution among countries. Res. Lett., 12 (11), 114030,
  11. PRIMAVERA, 2018: Primavera: User Interface Platform. Accessed 6 December 2018,


Posted in Climate, Energy meteorology, Renewable energy | Leave a comment

Steps To Make Sea Ice Projections More Robust

By David Schroeder

Climate model projections are our best approach to make predictions about future sea ice in both hemispheres for the whole 21st century. Given the large spread between individual model projections and distinct discrepancies between model results and satellite-based observations for the last decades, the question arises how accurate these projections are. The reliability of such simulations depends on how well key processes are captured in the climate model. CMIP5 (Coupled Model Intercomparison Project Phase 5) models generally simulate the observed sea ice decline better than the older CMIP3 models indicating that, for example, the improved representation of sea ice processes is important (Stroeve et al., 2012).

In Danny Feltham’s sea ice group we have added and improved several sea ice processes in the Los Alamos sea ice model CICE which is used in many climate models (e.g. the current U.K. Met Office model HadGEM3). The most important contributions are a physical melt pond model describing the evolution of melt ponds (Flocco et al., 2012; Schroeder et al., 2014), an elastic anisotropic plastic rheology accounting for the subcontinuum anisotropy of the sea ice cover (Tsamados et al., 2013) and a spatially and temporally variable form drag parameterization for the momentum exchange between ice and atmosphere as well as ice and ocean (Tsamados et al., 2014).

These new developments increase the realism of the sea ice model. However, incorporating these developments in a climate model does not result in better climate simulations as a matter of course. Many physical and empirical parameters are not very well constrained in a sea ice model requiring a tuning process applying observational data. In the past, most of the tuning has been based on sea ice concentration data derived from passive microwave imagery. This is a problem because realistic sea ice thickness is crucial for simulating variability and trends of the sea ice cover. Using radar altimetry, sea ice thickness estimates are available from CryoSat-2 (CS2) (e.g. from the Centre for Polar Observation and Modelling) during the ice growth seasons since 2010. However, the discrimination of ice and open water, the discrimination of ice type, retracking radar waveforms to obtain height estimates, constructing of sea surface height beneath the ice, and estimating the depth of the snow cover result in a grid cell ice thickness uncertainty of about 25 %.

Figure 1:  Mean effective sea ice thickness over read region shown in inlet above. Verification of CICE-default and CICE-best with Cryosat CS2 CPOM. Results from the ocean sea-ice model PIOMAS are added for comparison. Red area is defined as region where impact of winter growth on thickness change dominates other factors and where CS2 data are most accurate.

In a current study we selected an optimal region for comparing sea ice thickness between simulations with the sea ice model CICE and CS2 data (see red region in Figure 1) by taking into account the strengths and weaknesses of both approaches excluding locations where the number of CS2 observations is limited and the sea ice is thin (Schroeder et al., 2018). Figure 1 shows that a default CICE simulation (blue) strongly underestimates ice thickness, but a new CICE setup (red) agrees well. The most important impact comes from implementing a new process: accounting for the loss of drifting snow. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. In spite of its uncertainties we successfully applied CS2 sea ice thickness data to improve sea ice model physics.

Reducing uncertainty in sea ice projections requires better representation of key processes, not only of the sea ice component, but also in the atmosphere, the ocean and regarding the coupling. Polar cloud formation and the interaction with the sea ice surface has been identified as a key process which is currently not represented satisfactorily.


Flocco, D., D. Schroeder, Feltham D. L., Hunke E. C., 2012: Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007. J. Geophys. Res., 117, C09032,

Schroeder, D., Feltham D. L., Flocco D., Tsamados M., 2014: September Arctic sea-ice minimum predicted by spring melt-pond fraction. Nat. Climate Change, 4, 353-357,

Schröder, D., Feltham D. L., Tsamados M., Ridout A., Tilling R., 2018: New insight from CryoSat-2 sea ice thickness for sea ice modelling. The Cryosphere Discuss. (in review),

Stroeve, J. C., Kattsov V., Barrett A., Serreze M., Pavlova T., Holland M., Meier W. N., 2012: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett., 39, L16502,

Tsamados, M., Feltham D. L., Schroeder D., Flocco D., Farrell S. L., Kurtz N., Laxon S. W., Bacon S., 2014: Impact of Variable Atmospheric and Oceanic Form Drag on Simulations of Arctic Sea Ice. J. Phys. Oceanog., 44, 1329-1353,

Tsamados, M., Feltham D. L., Wilchinsky A., 2013: Impact of a new anisotropic rheology on simulations of Arctic sea ice. J. Geophys. Res.: Oceans, 118, 91-107,


Posted in antarctica, Arctic, Climate, Climate change, Climate modelling | Leave a comment

Seasonal Forecasting and the 2018 European Heatwave

By Len Shaffrey

The summer of 2018 has been one of the warmest on record in the UK and Europe. Warm temperatures over the summer led to impacts on agriculture, water resources and human health. One interesting question is how predictable was the 2018 European summer heatwave?

The skill of wintertime seasonal forecasts has dramatically improved in the past few years, especially for forecasts of the wintertime North Atlantic Oscillation (e.g. Scaife et al. 2014, Baker et al. 2018). However, there is much less skill for summertime seasonal forecasts for Europe. Interestingly, there was a substantial degree of convergence in the seasonal forecasts for summer 2018, with most forecast systems predicting warmer than average European temperatures. Figure 1 shows the multi-model mean seasonal forecasts of 2018 July-August-September(JAS) 2m temperature anomalies (forecasts were initialised around the 1 June 2018). The seasonal forecasts were predicting temperature anomalies of approx 1C over Eastern Europe. This led the Met Office to make the unusual step of issuing a statement on this summer’s forecast, which was picked up by the media.

Figure 1. Multi-model mean seasonal forecast of 2018 JAS 2m temperature anomalies from the Copernicus C3S seasonal forecast service.

It’s not yet clear why the summer of 2018 was more predictable than usual. This is an active research area which is being addressed in NERC projects such as SUMMERTIME and IMPETUS. Recent studies have highlighted how springtime North Atlantic seas surface temperature drive summertime European atmospheric circulation (Osso et al. 2018), the relationship between springtime Tropical Atlantic rainfall anomalies and summertime circulation over the North Atlantic (Wulff et al. 2017) and suggested that some seasonal forecasting systems can capture interannual variations in European summertime rainfall (Dunstone et al. 2018). Overall, these studies have suggested that there might be much more seasonal predictability for summertime European climate than previously thought. The studies also raise the possibility that deeper understanding of these processes may lead to substantially improved seasonal forecasts of summertime European climate.


Baker, L. H., Shaffrey, L. C., Sutton, R. T., Weisheimer, A. and Scaife, A. A, 2018: An intercomparison of skill and overconfidence/underconfidence of the wintertime North Atlantic Oscillation in multimodel seasonal forecasts. Geophysical Research Letters, 45 (15), 7808-7817.

Dunstone, N., Smith, D., Scaife, A., Hermanson, L., Fereday, D., O’Reilly, C., et al. 2018:. Skilful seasonal predictions of summer European rainfall. Geophysical Research Letters, 45, 3246–3254,

Osso, A., Sutton, R., Shaffrey, L. and Dong, B. 2018: Observational evidence of European summer weather patterns predictable from spring. Proceedings of the National Academy of Sciences of the United States of America, 115, 59-63. 

Scaife, A. A., et al. 2014: Skillful long‐range prediction of European and North American winters. Geophys. Res. Lett.,41, 2514–2519. doi: 10.1002/2014GL059637

Wulff, C. O., Greatbatch, R. J., Domeisen, D. I. V., Gollan, G., & Hansen, F. 2017:. Tropical forcing of the Summer East Atlantic pattern. Geophysical Research Letters, 44, 11,166–11,173.

Posted in Atlantic, Atmospheric circulation, Climate, Climate change, Climate modelling, Environmental hazards, Historical climatology, Hydrology, Numerical modelling, Seasonal forecasting, Waves | Leave a comment

Is it a normal season this year for tropical cyclones in the Western North Pacific?

By Xiangbo Feng

 The Western North Pacific (WNP) is the most active area for tropical cyclones (TCs).  The number of TCs occurred in the WNP so far (end of October) this year is 26 – just the average number of annual TCs over 1980-2017 (Figure 1 upper). As TC season in the north Hemisphere usually finishes up in October, we thus might think we are having another normal or slightly above-normal TC season for TC occurrence in the WNP.

Figure 1: Top: Western North Pacific (WNP) (0-60°N, 100-180°E) tropical cyclone (TC) count during the period 1980-2018. The 2018 TC record is updated to 31st October. Black, blue and green bars show annual, early summer (June-August, JJA) and late summer (September-October, SO) TC counts, respectively, while red bar is for the number of TCs that occur just in the northwestern sector (20-40°N, 120-140°E) of the WNP. Dashed lines are the long-term averages over 1980-2017. Bottom: Monthly Nino3.4 index anomaly during 1980-2018. Nino3.4 is the average sea surface temperature anomaly in the region of 5°N-5°S, 170°-120°W. Data sources: TC data are the Best Track observational TC record, retrieved on the 5th October 2018, from the Japan Meteorological Agency. Nino3.4 is obtained from NOAA PSD , retrieved on the 5th November 2018.

But, statistics from observations show a few interesting points that make this year not normal for TC activities(Only the TCs of tropical storm intensity or greater are considered here, i.e. maximum sustained wind speed ≥ O34 knots). 

  • First, it’s an active early summer (June-August, JJA) for TCs in the WNP (Figure 1 upper). In JJA, the long-term average number of named TCs over this area during 1980-2017 is 11, whilst the past early summer saw 17 tropical storms being named, 6 above the average, making it the second largest (it’s just 1 TC less than in 1994) on record since 1980.
  • Second, it then turned into a quiescent late summer (September-October) for TC occurrence. Late summer is usually a busy period when strong TCs (e.g. super typhoons) occur. The average number of TCs occurred during late summer is 8. There were only 6 TCs declared in the past late summer. More interestingly, the past October was very quiet in the WNP, featured with only one TC (Typhoon Yutu, formed on 21st October) declared. So far, it seems that some TCs, which were supposed to occur in a later time, were formed in an earlier time, making it a very busy early summer.

Figure 2: TC occurrence anomaly (with long-term average removed) in JJA for each year in the Western North Pacific. Bottom right shows the average of TC occurrence in JJA over 1980-2018.

  • Third, at regional scale (Figure 2), we see the past early summer is actually an extremely busy period for TCs especially in the northwestern parts (20-40°N, 120-140°E). The climatology of the number of TCs passing through this region in early summer is 6 (also see Figure 1 upper). But, there were 12 TCs coming through this region in JJA this year, double the average number. In relation to the normal path of TCs, e.g. through the Philippine Sea and Taiwan (Figure 2 bottom right), this year’s TCs tend to shift northward heading to the north of China and the west of Japan. Similar path years are 1994, 2002 and 2004, but they have smaller positive anomalies.

Seasonal and sub-seasonal TC forecasts

Figure 3: Correlations between JMA observed TC occurrence anomaly and Nino3.4 index, for annual values (top), early summer (June-August, JJA) values (bottom left) and late summer (September-October, SO) values (bottom right), over 1951-2018. Dot areas are where the correlations pass the 0.1 significance test (90% confidence level).

It has been well documented that the El Niño–Southern Oscillation (ENSO) plays an important role in the inter-annual variability of TC activity (especially for the TC lifetime) via modulating the large-scale atmospheric environments (e.g. wind shear and relative humidity). As the effect on number of TCs, for example (Figure 1), in 1998, when a strong El Nino event was developed, fewer TCs are formed in the WNP over the whole year; in the 2015-2016 El Nino event, TCs are more likely to recurve to north rather than straightforwardly head to west. The teleconnections of TC occurrence with ENSO phases can be simply described by the correlation coefficient map (Figure 3 top).

Some meteorological services and agencies have included ENSO as one main source of predictability for the WNP TC activities in their seasonal forecast outlooks. Among these services are the GCACIC forecast at the City University of Hong Kong and the TSR forecast at the University College London. Before TC season started this year, they had forecasted that this year would be a normal or slightly above-normal year for the WNP TC occurrence. This is mainly due to the cold ENSO-neutral condition (Figure 1 bottom). In the meantime, the Met Office’s dynamical seasonal forecast model GloSea5 also predicts a normal or slightly below-normal season for the WNP TC occurrence (contact me if you want to see this result). It seems like that those seasonal forecasts did a reasonably good job for predicting the total number of TCs for the whole season.

However, none of those forecasts, as far as I know, had predicted either an active early summer or a quiescent TC late summer for TCs as observed, even with a short forecast lead time. This means that the present seasonal forecast models (both statistical and dynamical) have less ability to predict the TC activities at sub-seasonal timescale (a time range between two weeks to two months). One of reasons for lack of such predictability might be due to the TC activities, which are ENSO-dependent in late summer, but are ENSO-independent in early summer (Figure 3 bottom). Even though the ENSO states are reasonably estimated, it does not necessarily mean that the most part of TC occurrence variability at sub-seasonal timescale can be well predicted.

The apparent sub-seasonal variations of TC activities, at least this year in the WNP, indicate the necessity of developing sub-seasonal TC forecast approaches on top of the normal seasonal forecast. Including some factors at sub-seasonal timescales, such as the Madden–Julian Oscillation and the equatorial waves, may help us to build up such forecast skills. This indicates an area where more methodological work needs to be carried out in the future. Recently, some efforts have been made to address this challenge, such as the WWRP/WCRP Sub-seasonal to Seasonal Prediction Project (S2S)

Here in Reading, the FASCINATE project, one of the Weather and Climate Science for Service Partnership (WCSSP) Southeast Asia projects funded by the Newton Fund (Nick Klingaman is the PI), is carrying out a detailed analysis of TC activities in the WNP. One aim of our project is to identify the sources of predictability for the WNP TCs, from synoptic to seasonal timescales, and evaluate the present forecast systems, such as the Met Office’s Numerical Weather Prediction and GloSea5, on the representations of such prediction sources.

Posted in Atmospheric circulation, Climate, Climate modelling, earth observation, ENSO, Equatorial waves, Historical climatology, Madden-Julian Oscillation (MJO), Numerical modelling, Seasonal forecasting, Tropical cyclones, Waves, Weather forecasting, Western North Pacific, Wind | Leave a comment